![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2007/10/brachiosaurus-brancai-c8.jpeg\")
Where it all began, from a certain point of view.
Where it all began, from a certain point of view.
Today is SV-POW!’s birthday. Not a nasty, dirty, wet birthday, like a 2nd or a 13th, nor yet a grand, nice-round-number birthday like a 10th or a 20th: it is our 19th birthday, and that means we’ve been around a while. I decided to celebrate by posting surprising facts about RNA.
\nNah, that was a transparent and rather feeble misdirect. It’s going to be sauropod vertebrae! (Although if you were really hoping for a surprising fact about RNA, hit me up in the comments.)
\nWe’ve talked here about sauropod neck muscles, most recently in the posts linked here, and for obvious reasons. Witmer (1997) evocatively described the form of pneumatic bones as the result of a sort of morphogenetic tug-of-war, in which pneumatic diverticula expand opportunistically and invasively and tend to resorb bone tissue that isn’t actively being maintained, and bone tissue grows and is maintained in response to biomechanical stress. That paper landed almost 30 years ago, right after I’d started studying sauropod vertebrae (in 1996, story told here) and right before I got to start looking at CT scans of sauropod vertebrae (in 1998, story told here), it was one of the first things I read when I started working on pneumaticity, and it’s been part of my intellectual bedrock ever since.
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2022/02/diplodocus-diverticula-schwarz-et-al-2007-fig-4.jpg\")
The jacket of pneumatic diverticula around a cervical vertebra of Diplodocus, from Schwarz et al. (2007: fig. 4).
It struck me with the force of inevitability that the shapes of sauropod vertebrae were driven by a small constellation of factors: supporting great weight and long necks and tails — mostly compressive forces; resisting or accommodating the forces exerted by muscles, tendons, and ligaments — mostly tensile, at least up close;* doing those thing with the same basic developmental Lego kit as virtually all other vertebrates (as touched on here, and, er, way back here) — but with the spicy addition of pneumatic diverticula, with their rabid tendency to cart away everything not nailed down. Or more succinctly:
\nvertebra shape = development + size + muscles – pneumatization
\n*Two or more tension members can load a bony element in compression by pulling on opposite sides to snug it down tight to the next bone in line, but where the muscle or tendon fibers actually attach, the force exerted on the bone tissue will be primarily tensile.
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2019/06/img_4537.jpg\")
I look at this Barosaurus vertebra (at the Museum of Ancient Life in Lehi, Utah) and I feel like I can almost see the code.
It is almost a haunting thought — if diverticula have removed all the bone that isn’t being loaded,* then the forms of sauropod vertebrae should be their biomechanical loading regimes incarnate, and it feels like we should be able to easily visualize where all the muscles, tendons, and ligaments attached. Alas, that “easily” is more of a “hardly”, because vertebrae are kind of a biomechanical nightmare of complex forces and lots and lots of muscles, big and small, and we’re still uncertain about some basic stuff like which vertebrae had synovial intervertebral joints and which had fibrocartilaginous discs (birds have both in the same vertebral column), and there are probably limits to how far we can get just tracing bird muscles (or croc muscles, or what have you) onto sauropod vertebral columns, not least because birds are spectacularly diverse, sauropods were pretty darned disparate themselves, as well as freakin’ huge, and those two lineages parted ways well over 200 million years ago.
\n*To a first approximation — there are exceptions to the hypothesis of “optimization by pneumatization” in its purest form (f’rinstance).
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2026/10/chicken-neck-muscles-zweers-et-al-1987-fig-7.jpg\")
How complex are we talkin’? Well, here’s a very simple, diagrammatic map to the neck muscles of the chicken, from Zweers et al. (1987: fig. 7). Not multiple chickens, like different individuals or breeds, just…a chicken. Any chicken. Every chicken. Even drawing each slip of muscle as a line, and packing things together almost to the limit of readability, it still took four different diagrams to show the muscles on one side of one neck from one angle. In a chicken. A chicken!!
\nEDIT after I’d slept: Actually it’s even worse, because that multi-part diagram only shows the multi-segment muscles, the ones that cross more than one intervertebral joint. All the single-segment muscles that connect adjacent vertebrae, like the interspinales, intercristales, and intertransversarii, are omitted for clarity.
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2019/11/skyward.jpg\")
The awesome mounted Brachiosaurus skeleton that used to stand outside the Field Museum — a wonder that this blog has sadly outlived. From our sixth-ever post.
So when in future posts in this series I talk blithely about the longus colli dorsalis muscles of sauropods inserting on their epipophyses, or anything along those lines, I want you to do three things: (1) remember that simple, line-based diagram from Zweers, that isn’t even trying to represent the muscles realistically, but only to map their connections; (2) think about trying to translate that to animals with necks as long as tour buses; and (3) take everything that I or anyone else ever says on this topic with an evaporated ocean of salt.
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2023/11/figure-7-diplo-apato-muscle-comparison.jpeg\")
Reasonable inference, or a hopeful monster? Wedel and Taylor (2023: fig. 7).
But still. We oughta be able to figure some things out, right? Stay tuned.
\nThanks for reading, whether you’ve been here from the start or you’re one of the readers we picked up along the way (at this point, possibly because you were born along the way). And thanks to Mike and Darren, for a conversation 19 years ago that hatched a joke that became a blog that wholly unexpectedly evolved into one of the biggest and most rewarding things I’ve done, and for all the years of inspiration, support, and friendship along the way. Next round’s on me.
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2026/09/dinocon-2026-matt-darren-and-mike.jpg\")
The SV-POW!sketeers at DinoCon 2026 — about which more real soon.
![](\"https://medical-education.pages.ub.uni-bielefeld.de/research/mes-blog/posts/2026-10-01-PISA-schock/FriederichsH_pisa-schock.png\"/)
Letzten Monat hat die OECD die Ergebnisse von PISA 2025 ver\u00f6ffentlicht, und sie sind die schlechtesten seit Beginn der Studie. Ein Vierteljahrhundert nach dem ersten \"PISA-Schock\" stellt sich f\u00fcr Lehrende und Forschende in der Medizin die Frage, ob uns das \u00fcberhaupt etwas angeht. Die kurze Antwort lautet ja, aber an anderer Stelle, als die Schlagzeilen vermuten lassen.
\nAls im Dezember 2001 die Ergebnisse der ersten PISA-Erhebung erschienen, lag Deutschland in allen drei getesteten Bereichen unter dem OECD-Durchschnitt. Der Begriff \"PISA-Schock\" war schnell gepr\u00e4gt und hat sich als Vokabel gehalten. Am 8. September 2026 ist nun der erste Band zu PISA 2025 erschienen. Mehr als 760.000 15-J\u00e4hrige aus 91 L\u00e4ndern und Volkswirtschaften haben teilgenommen, und die mittleren Leistungen im OECD-Raum liegen in Naturwissenschaften, Lesen und Mathematik so niedrig wie in keiner Erhebung zuvor (OECD, 2026). Deutschland liegt in den Naturwissenschaften leicht \u00fcber, im Lesen und in Mathematik in etwa auf diesem gesunkenen Durchschnitt.
\nWer an einer Medizinischen Fakult\u00e4t lehrt oder forscht, k\u00f6nnte das achselzuckend zur Kenntnis nehmen. Unsere Studierenden geh\u00f6ren schlie\u00dflich zu den Abiturbesten, und unsere Patient*innen sind l\u00e4ngst aus der Schule. Beides stimmt, und beides greift gleichzeitig zu kurz.
\nPISA testet seit dem Jahr 2000 alle drei Jahre 15-J\u00e4hrige in Lesen, Mathematik und Naturwissenschaften. Die Aufgaben sind so konstruiert, dass sich die Ergebnisse \u00fcber L\u00e4nder und Erhebungsjahre hinweg auf einer gemeinsamen Skala vergleichen lassen, pro Land nehmen mindestens 150 Schulen und 4.500 Sch\u00fcler*innen teil, und die \u00dcbersetzung in \u00fcber 40 Sprachen wird eigens qualit\u00e4tsgesichert (Turner & Adams, 2007). Gepr\u00fcft wird nicht Schulstoff, sondern die F\u00e4higkeit, Gelesenes und Gerechnetes in Alltagssituationen anzuwenden. Die OECD nennt das \"literacy\", die deutschen PISA-Berichte \u00fcbersetzen es mit \"Grundbildung\", also mit dem, was man zum Zurechtkommen im Alltag braucht.
\nDrei f\u00fcr die Medizin wichtige Dinge misst PISA allerdings nicht, n\u00e4mlich Studierf\u00e4higkeit, Eignung f\u00fcr ein Medizinstudium und Gesundheitskompetenz. Die Teilnahme ist au\u00dferdem anonym. Niemand kann die PISA-Punkte einer Person mit ihren sp\u00e4teren Studienleistungen verkn\u00fcpfen, und entsprechend gibt es keine einzige Studie, die das versucht h\u00e4tte. So k\u00f6nnen PISA-Ergebnisse eigentlich nichts \u00fcber k\u00fcnftige Medizinstudierende voraussagen.
\nDie wichtigste Wirkung des ersten PISA-Schocks auf die Medizinausbildung ist indirekt. Die bildungspolitische Antwort auf 2001 war die Umstellung des Schulsystems auf Bildungsstandards und Kompetenzorientierung, festgehalten in der Klieme-Expertise von 2003 und gest\u00fctzt auf den Kompetenzbegriff von Franz Weinert, n\u00e4mlich Kompetenz als erlernbare F\u00e4higkeit, Probleme in wechselnden Situationen zu l\u00f6sen. Das ist genau die Sprache, in der heute der Nationale Kompetenzbasierte Lernzielkatalog Medizin geschrieben ist.
\nEin Nebenprodukt der PISA-\u00c4ra war die Verk\u00fcrzung der Gymnasialzeit auf acht Jahre. Die Bef\u00fcrchtung, G8-Absolvent*innen k\u00e4men schlechter vorbereitet an die Hochschule, hat sich in Vergleichen der Doppeljahrg\u00e4nge nicht best\u00e4tigt. F\u00fcr die Medizin passen dazu die L\u00e4ngsschnittdaten einer Heidelberger Studie. Die Abiturnote war dort der st\u00e4rkste Pr\u00e4diktor f\u00fcr die Studienleistung, das Alter bei Studienbeginn hing vor allem mit dem Abbruchrisiko zusammen (Kadmon et al., 2014). Der Test f\u00fcr Medizinische Studieng\u00e4nge (TMS) sagte die vorklinische Leistung sogar besser vorher als die Abiturnote, und Bewerber*innen mit mittelm\u00e4\u00dfigem Abitur und sehr gutem Testergebnis erreichten das Niveau der Abiturbesten (Kadmon & Kadmon, 2016).
\nAuch interessant ist, was die PISA-Daten \u00fcber den Weg ins Studium verraten. PISA fragt die 15-J\u00e4hrigen auch nach ihrem Wunschberuf, und \"\u00c4rzt*in\" geh\u00f6rt seit der ersten Erhebung durchgehend zu den meistgenannten Antworten. In einer Mehrebenenanalyse der Daten von 2018 aus 61 Volkswirtschaften erwarteten rund elf Prozent der Jugendlichen, einmal als \u00c4rzt*in zu arbeiten, aber nur zwei Prozent in der Pflege (Guo & Hau, 2023).
\nDie OECD hat das 2025 fortgeschrieben. Zwischen 2018 und 2022 ist der Anteil der 15-J\u00e4hrigen mit Berufswunsch Medizin in etwa der H\u00e4lfte der OECD-L\u00e4nder zur\u00fcckgegangen, und unter denen, die diesen Wunsch haben, sind inzwischen fast drei Viertel M\u00e4dchen (OECD, 2025b).
\nDie meisten 15-J\u00e4hrigen von 2025 sitzen 2055 nicht im H\u00f6rsaal, aber gegebenenfalls in der Sprechstunde. Im OECD-Durchschnitt erreichte ein F\u00fcnftel von ihnen in keinem der drei Bereiche das Basisniveau, drei Jahre zuvor war es noch ein Sechstel (OECD, 2026). Diese Jugendlichen k\u00f6nnen einen mittellangen Text nicht sicher erschlie\u00dfen. Bei den Erwachsenen sieht es nicht besser aus. In der OECD-Kompetenzerhebung liest fast ein Viertel der 25- bis 64-J\u00e4hrigen in Deutschland auf oder unter der untersten Stufe, versteht also nur sehr kurze Texte mit wenig ablenkender Information, und bei Erwachsenen ohne Abschluss der Sekundarstufe II ist die Lesekompetenz seit der vorigen Erhebungsrunde deutlich gesunken (OECD, 2025a).
\nGesundheitskompetenz misst PISA nicht, das ist eine wichtige Abgrenzung. Die einschl\u00e4gige Forschung nutzt eigene Instrumente. Die HBSC-Studie (\"Health Behaviour in School-aged Children\", eine regelm\u00e4\u00dfige Schulbefragung unter dem Dach der WHO) zeigt f\u00fcr Deutschland, dass ein Viertel der 11- bis 15-J\u00e4hrigen eine niedrige Gesundheitskompetenz hat, etwas mehr als vor der Pandemie, und dass niedrige Gesundheitskompetenz mit deutlich h\u00e4ufigeren psychosomatischen Beschwerden einhergeht (Sendatzki, 2024). Lesekompetenz ist dabei kein Synonym f\u00fcr Gesundheitskompetenz, aber ihr Substrat. Wer einen Beipackzettel, einen Arztbrief oder eine Aufkl\u00e4rung nicht lesen kann, kann sie auch nicht verstehen.
\nHier liegt die Relevanz des PISA-Schocks f\u00fcr die Medizin. Nicht in der Frage, wer k\u00fcnftig im H\u00f6rsaal sitzt, sondern in der Frage, mit wem die Absolvent*innen sp\u00e4ter sprechen. Kommunikationstraining, verst\u00e4ndliche Sprache und Methoden wie Teach-back, bei dem Patient*innen das Geh\u00f6rte in eigenen Worten wiedergeben, sind in den Curricula angekommen. Die PISA-Daten liefern die Begr\u00fcndung daf\u00fcr, sie nicht als weiches Beiwerk zu behandeln.
\nPISA 2025. Mehr als 760.000 Sch\u00fcler*innen aus 91 L\u00e4ndern und Volkswirtschaften. Im OECD-Durchschnitt sank die Lesekompetenz zwischen 2015 und 2025 um 28 Punkte (489 auf 461), Mathematik um 22 Punkte (485 auf 463), Naturwissenschaften um 7 Punkte (489 auf 482). Der Anteil der Sch\u00fcler*innen, die in keinem Bereich das Basisniveau 2 erreichten, stieg von 16 % (2022) auf 20 %. Deutschland erreichte 486 Punkte in den Naturwissenschaften, 465 im Lesen und 464 in Mathematik (OECD, 2026). Zur Einordnung nennt die OECD selbst einen Richtwert, n\u00e4mlich dass 15-J\u00e4hrige \u00fcber ein Schuljahr typischerweise etwa 20 Punkte dazugewinnen, mit gro\u00dfer Streuung zwischen den L\u00e4ndern und ausdr\u00fccklich nur f\u00fcr Zeitvergleiche gedacht (OECD, 2026). Der R\u00fcckgang im Lesen seit 2015 entspricht danach grob eineinhalb Schuljahren, der in Mathematik etwa einem Schuljahr.
\nBerufswunsch Medizin. Guo und Hau analysierten PISA 2018 aus 61 Volkswirtschaften mit Mehrebenen-Logistik und hierarchischer linearer Regression. Systemfaktoren erkl\u00e4rten rund ein Drittel der Varianz der Berufsw\u00fcnsche, individuelle Hintergrundmerkmale 10 % (Arztberuf) bzw. 1 % (Pflege). In wohlhabenden L\u00e4ndern z\u00e4hlte die Arbeitssicherheit (Interaktion BIP \u00d7 Verletzungen \u03b2 = \u22120,342, p < 0,01), in \u00e4rmeren das Gehalt (BIP \u00d7 Gehalt \u03b2 = \u22120,374, p < 0,05) (Guo & Hau, 2023). Nach OECD-Angaben sank der Anteil 15-J\u00e4hriger mit Berufswunsch Medizin im OECD-Schnitt von 8,5 % (2018) auf 8,1 % (2022); 72 % der Interessierten waren 2022 M\u00e4dchen, 2015 waren es 69 % (OECD, 2025b).
\nStudierf\u00e4higkeit in der Medizin. Kadmon et al.\u00a0verfolgten in Heidelberg drei Kohorten (N = 720) bis zum PJ. Die Abiturnote sagte Studienverz\u00f6gerungen in der Vorklinik vorher (OR = 2,64), das Studieneintrittsalter das Abbruchrisiko (OR = 0,85). Mehr als 20 % der \u00fcber Wartezeit oder Nachr\u00fcckverfahren Zugelassenen brachen in der Vorklinik ab, und nur rund 35 % aller Zulassungsgruppen begannen das PJ in der Regelstudienzeit (Kadmon et al., 2014). In vier weiteren Kohorten prognostizierte der TMS die vorklinische Leistung mit \u03b2 = 0,44 gegen\u00fcber \u03b2 = 0,24 bis 0,05 f\u00fcr die Abiturnote; mittelm\u00e4\u00dfige Abiturient*innen mit TMS-Werten ab 125 erreichten das Niveau der Abiturbesten (Kadmon & Kadmon, 2016).
\nEin Lehrst\u00fcck in Sachen Fisch. Wie leicht sich PISA-Daten \u00fcberinterpretieren lassen, zeigt eine Arbeit aus dem Scandinavian Journal of Public Health. Die Autoren setzten die PISA-Werte von 64 L\u00e4ndern mit dem nationalen Fischkonsum in Beziehung und fanden, dass dieser auch nach Kontrolle f\u00fcr den wirtschaftlichen Entwicklungsstand noch vier Prozent der Varianz erkl\u00e4rte, was sie mit der Omega-3-Versorgung begr\u00fcndeten (Schmiedel et al., 2018). Die Analyse ist sauber gerechnet und trotzdem ein Paradebeispiel f\u00fcr den \u00f6kologischen Fehlschluss. Ein Zusammenhang auf L\u00e4nderebene sagt nichts dar\u00fcber, ob ein einzelnes Kind von mehr Fisch besser liest. Ein Regressionsmodell mit Internetabdeckung (\u03b2 = 0,65) und Fischkonsum (\u03b2 = 0,20) erkl\u00e4rte 72 % der Varianz der PISA-Werte von 64 L\u00e4ndern (adj. R\u00b2 = 0,72), der Fischkonsum davon zus\u00e4tzliche 4 %. Stillraten und BIP wurden nicht signifikant (Schmiedel et al., 2018). Eine einfache Korrelation von r \u2248 0,57 zwischen Fischkonsum und PISA-Wert klingt eindrucksvoll, ist aber ein Aggregatbefund \u00fcber L\u00e4nder, nicht \u00fcber Kinder.
\nLese- und Gesundheitskompetenz. In der OECD-Erhebung \u00fcber die Kompetenzen Erwachsener liegen 23 % der 25- bis 64-J\u00e4hrigen in Deutschland auf oder unter Lesekompetenzstufe 1 (OECD-Durchschnitt 27 %). Bei Erwachsenen ohne Sekundarstufe-II-Abschluss sank die mittlere Lesekompetenz zwischen den Runden 2012\u20132015 und 2023 von 222 auf 203 Punkte; die Kompetenzdifferenz zwischen 25- bis 34-J\u00e4hrigen mit Terti\u00e4rabschluss und ohne Sekundarstufe-II-Abschluss ist in Deutschland mit 100 (Lesen) bzw. 108 Punkten (Alltagsmathematik) die gr\u00f6\u00dfte aller teilnehmenden OECD-L\u00e4nder (OECD, 2025a). In der HBSC-Studie 2022 (N = 6.475) hatten 24,4 % der 11- bis 15-J\u00e4hrigen eine niedrige Gesundheitskompetenz (2017/18: 21,4 %); niedrige Gesundheitskompetenz war adjustiert mit hoher psychosomatischer Belastung assoziiert (OR = 2,64; 95 %-KI 2,12 bis 3,29) (Sendatzki, 2024).
\nTransparenzhinweis:
\n@misc{friederichs2026,\n author = {Friederichs, Hendrik},\n title = {Der PISA-Schock \u2013 relevant f\u00fcr die Medizin(ische\n Ausbildung)?},\n date = {2026-10-01},\n url = {https://medical-education.pages.ub.uni-bielefeld.de/research/mes-blog/posts/2026-10-01-PISA-schock/},\n langid = {de}\n}\nBetween 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference, jointly organised by the Nordic Society Oikos and the Ecological Society of Germany, Austria and Switzerland (Gf\u00d6) in Odense, Denmark.
\n\n\n\nThemed \"Integrating the Diversity of Ecology\", the event welcomed over 1,000 researchers in the field from across career stages, who had the opportunity to enjoy conference talks across 36 thematic sessions, as many as six keynote talks, two poster sessions, several open society meetings and quite a few social events, including excursions and a conference dinner.
\n\n\n\n![\"\"](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260917_120130-scaled.jpg?ssl=1\")
Throughout the scientific event, conference delegates had the opportunity to also explore the various publishing services offered by Pensoft to authors of research, journal managers, conference organisers; the diverse portfolio of almost 100 open-access peer-reviewed journals published on Pensoft's ARPHA Publishing Platform.
\n\n\n\nBeyond publishing solutions, a key focus was given to OBSGESSION: a Horizon Europe-funded project where Pensoft is actively involved. To better understand and predict changes in terrestrial and freshwater ecosystems, the OBSGESSION consortium integrates multi-source Earth Observation data (such as high-resolution satellite imagery and drone monitoring) with ground-based in-situ research, including advanced environmental DNA (eDNA) sampling. By coupling these data streams with cutting-edge detection-attribution modelling and machine learning algorithms, the project generates scalable Essential Biodiversity Variables (EBVs). These ecological approaches and analytical frameworks are actively tested and validated through six biodiversity pilots across diverse European ecosystems. In the open-science Research Ideas and Outcomes journal, you can find a collection of project outputs, including research papers, project reports and deliverables, and guideline papers published across journals and platforms.
\n\n\n\nAs a leader of the \"Dissemination, Multi-stakeholder outreach and synergies\" work package at OBSGESSION, Pensoft is tasked with stakeholder engagement and community building, ultimately contributing to the high impact of the resulting outputs, methodologies, and policy recommendations that aim to strengthen public understanding of biodiversity change.\u00a0\u00a0
\n\n\n\nAs a leader of the \"Dissemination, Multi-stakeholder outreach and synergies\" work package at OBSGESSION, Pensoft is tasked with stakeholder engagement and community building, ultimately contributing to the high impact of the resulting outputs, methodologies and policy recommendations that aim to strengthen public understanding of biodiversity change.
\n\n\n\n*
\n\n\n\nOne of the six keynote talks was presented by Prof. Dr. Isabell Hensen (Martin Luther University Halle, Germany), who received the Gf\u00d6 Honorary Medal 2026 for her leading, forward-looking contributions to ecological research and the scientific community. Her work examines how genetic variation, environmental conditions, disturbance, and plant-animal interactions shape plant populations, with particular emphasis on rare species, biological invasions, and the regeneration of degraded forests.
\n\n\n\n![](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260914_163906-scaled.jpg?w=840&ssl=1\")
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In her talk, titled \"One spark is enough: regeneration ecology of Andean forests under global change\", she reported on post-fire regeneration processes in tropical Andean forests, based on long-term field experiments and observational studies.
\n\n\n\nHensen focused on the well-studied endemic plant genus Polylepis, which forms the world's highest-elevation forests and illustrates how fire has shaped ecosystem dynamics over millennia. Her talk also mentioned a 2022 monograph of the genus, published in the Pensoft journal PhytoKeys by Tatiana Erika Boza Espinoza (Pontificia Universidad Cat\u00f3lica del Per\u00fa) and Michael Kessler (University of Zurich), which brought together results from 35 years of field and herbarium experience.
\n\n\n\n*
\n\n\n\nOn Wednesday, Pensoft's Iva Boyadzhieva delivered a presentation titled “Scholarly Communication: Lessons from the Publisher’s Kitchen”. She highlighted the vital role publishers play in helping researchers communicate their insights not only to peers, but also to decision-makers and the public. Emphasizing the publisher’s responsibility to support authors with robust infrastructure and dedicated human expertise, she outlined how Pensoft works to FAIRify scholarly publishing and future-proof scientific knowledge in an era of rapid AI development and expanding research output.
\n\n\n\n![](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/Nordic-gfo-talk-by-Iva-2026-10-1.png?w=840\")
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*
\n\n\n\nPensoft's team was delighted to meet up with dedicated readers, authors, reviewers, and editors from across Pensoft's portfolio, as well as introduce Pensoft journals to attendees discovering them for the first time.
\n\n\n\n![](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260914_113802-scaled.jpg?w=840\")
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Unsurprisingly, well-established society journals published by Pensoft, such as Frontiers of Biogeography, NeoBiota, Vegetation Classification and Survey, Vegetation Ecology and Diversity and Italian Botanist drew plenty of attention, alongside long-standing Pensoft's own titles like Nature Conservation, PhytoKeys, One Ecosystem and the Biodiversity Data Journal.
\n\n\n\nVisitors were also particularly intrigued by several newly launched Pensoft journals. Developed in close collaboration with passionate researchers, these fresh outlets directly address key gaps in academic publishing.
\n\n\n\nOne of the newly launched journals in the spotlight was Agricultural and Ecological Modelling (AEM), which provides a unique scholarly outlet thanks to its modern approach to publishing by increasing transparency, giving credit to research objects across the entire modelling research lifecycle. This is achieved through publishing specialised article types, such as: Formal Model, Model Testing and Calibration, Model Implementation and Documentation, Data Papers and Software Descriptions. These new publishing formats are designed to keep pace with advances in Modelling research and to make it FAIR (Findable, Accessible, Interoperable, and Reusable) by enabling scholarly credit for diverse research outputs, often created by different groups of authors. For the time being, AEM remains a Diamond Open Access journal, meaning that it will not charge authors for open-access publication.
\n\n\n\nOther recently founded journals addressing emerging and fast-developing disciplines within the ecological field that took the spotlight were:
\n\n\n\nAll of these journals operate with no article fees for either authors, nor readers.
\n\n\n\nTo receive updates on new publications and relevant news, sign up for Email Alerts on any journal's homepage. Creating a Pensoft account also allows users to easily customise their notification preferences, follow additional journals, or filter updates by research topic. You can also follow journals on social media, including BlueSky, Facebook and Linkedin.
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\n\n\n\nDelegates at the event showed strong interest in OBSGESSION's first policy brief: \"Prioritising Essential Biodiversity Variables (EBVs) to support European Union (EU) policy needs based on the EU's Biodiversity Strategy for 2030.\" To tackle a critical challenge in environmental monitoring, OBSGESSION focuses on prioritising key EBVs to directly support the EU Biodiversity Strategy 2030 – a roadmap designed to put Europe's biodiversity on a path to recovery through 102 specific actions and sub-actions.
\n\n\n\nTo align these policy needs with actionable data, the OBSGESSION team conducted an assessment to identify priority EBV candidates. By categorising EBV-applicable actions and indicators across different ecosystem realms and implementation stages, the project pinpointed the most essential variables needed to strengthen monitoring, reporting, and policy execution. The document is based on a deliverable by the project (D1.1 Policy Landscape and Needs Assessment).
\n\n\n\nThis policy brief is part of a broader, ongoing effort within OBSGESSION to bridge the gap between biodiversity data and policy execution. To dive deeper into the science behind this topic, the project has hosted a dedicated seminar series exploring key aspects of the framework:
\n\n\n\nBeyond theoretical frameworks, OBSGESSION grounds its research in six biodiversity validation pilots across Europe. The case studies represent European biodiversity and ecosystem conservation challenges at scales varying from local to regional. Full details on each ecosystem pilot are now available in a collection of newly published pilot factsheets, which made their debut at the conference, where people could take a look at their digital versions.
\n\n\n\nReading those materials gives an insight into the practical testing environment to demonstrate how Earth observation, field research, and ecological modelling come together to deliver actionable biodiversity insights. You can also check out OBSGESSION's biodiversity pilots on the project's website.
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\n\n\n\nTo relive the conference by exploring the detailed programme alongside the full abstracts, visit the conference website.
\n\n\n\nKeep up with future updates and news from Pensoft by following us on Bluesky, Facebook, X, and LinkedIn.
\n\n","doi":"https://doi.org/10.59350/0261d-tmz21","guid":"https://blog.pensoft.net/?p=20944","image":"https://blog.pensoft.net/wp-content/uploads/2026/09/SciComm-PR-2026-Instagram-2026-09-25T164847.347-e1790348402124.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790640000,"rid":"1zdgz-txj12","summary":"Between 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference in Odense, Denmark.","tags":["Advances In Pollinator Research","Agricultural And Environmental Modelling","Biodiversity Data Journal","BioRisk","Frontiers Of Biogeography"],"title":"Pensoft at the Nordic Oikos-Gf\u00d6 2026 conference: Integrating the Diversity of Ecology","updated_at":1790862913,"url":"https://blog.pensoft.net/2026/09/29/pensoft-at-the-nordic-oikos-gfo-2026-conference-integrating-the-diversity-of-ecology/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"name":"Pensoft Editorial Team"}],"blog":{"authors":null,"community_id":"1e54953c-587a-4743-89af-ebd2cb45660c","created":1789516800,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/pensoft","favicon":"https://rogue-scholar.org/api/communities/1e54953c-587a-4743-89af-ebd2cb45660c/logo","feed_format":"application/atom+xml","feed_url":"https://blog.pensoft.net/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blog.pensoft.net/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"pensoft","status":"active","subfield":"2303","title":"Pensoft Blog","updated":1790862491,"use_api":true},"blog_name":"Pensoft Blog","blog_slug":"pensoft","content_html":"\n\nLast week marked another annual meeting of the Biodiversity Information Standards network, otherwise known as TDWG, with the Norwegian capital Oslo hosting the event between 20 and 25 September.
\n\n\n\nTDWG is the international, non-profit organisation formerly known as the Taxonomic Databases Working Group, which develops and maintains the standards, such as Darwin Core, that allow biodiversity data to be shared, integrated and understood across institutions worldwide.
\n\n\n\nThe six-day programme was a meeting point for over 300 stakeholders from research, practitioner and corporate circles, with proceedings centred around one central topic – \"Research and Robot- Ready Biodiversity Data Standards\". In that sense, the themes of accessibility, responsibility, innovation and collaboration were foremost on the minds of participants as they contemplated how information on the biosphere should be extracted, consolidated and shared in the years to come.
\n\n\n\nAs a long-standing partner of TDWG, Pensoft Publishers served as both a sponsor and an exhibitor for the event. In the latter capacity, the company contributed with a joint booth for three of the EU-funded projects it is leading the outreach efforts for:
\n\n\n\n![\"\"](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260922_081409-1-scaled.jpg?ssl=1\")
A number of other projects from Pensoft's communication portfolio also found a place in the display, including BEAGLE, OneSTOP, B-Cubed, MAMBO, OBSGESSION and BioAgora. Furthermore, 14 of the company's journals were in the spotlight, including Biodiversity Information Science and Standards (BISS), the official journal of the TDWG community (where all of the conference's abstracts are set to be published).
\n\n\n\nCrucially, the event also saw the official launch of ORPHO, Pensoft's new collaborative workspace for scientific writing. Founder and CEO Prof. Lyubomir Penev and CTO Teodor Georgiev conducted live demos of the tool, showcasing how manuscripts crafted within it with full version control for authors become discoverable, interoperable and FAIR-compliant.
\n\n\n\nIn the context of TDWG2026's agenda, they also presented ORPHO's integrated semantic annotation and communication instruments as part of the workshop \"How Literature Services can Support & Benefit from Biodiversity Publication and Data Standards?\" on 24 September. The workspace was received with anticipation and enthusiasm by attendees, with many expressing a desire to test it out for themselves.
\n\n\n\nThey were also treated with a tease of Pensoft's next large-scale endeavour, as Senior Communications Officer Peter Bozakov pulled the curtain behind the forthcoming communication platform for research projects Scientiana.
\n\n\n\n\n\n\n\nAs for other notable contributions to the wider conference agenda by researchers affiliated with Pensoft's publishing or project activities:
\n\n\n\n![\"\"](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260921_155043-1-scaled.jpg?ssl=1\")
![\"\"](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/MVIMG_20260925_094652-1-scaled.jpg?ssl=1\")
Notably, the Biodiversity Information Science and Standards (BISS) journal is the home for TDWG conference abstracts. TDWG 2026 presenters can currently submit their abstracts there and publish them in an extended abstract or mini-paper format completely free of charge. The journal also hosts abstracts from conferences held over the past decade. You can browse the TDWG 2026 collection here.
\n\n\n\nLast but not least, the company's sponsorship supported two awards at TDWG2026 (with a free publication in a Pensoft journal as an accompanying prize):
\n\n\n\n![\"\"](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/MVIMG_20260925_164100_1-1-scaled.jpg?ssl=1\")
Overall, TDWG2026 proved itself a crucial forum for mapping out the future of biodiversity monitoring and the management of the myriad data streams that emerge from it. Pensoft is proud to be an important actor in this process, contributing with innovative tools and solutions at the heart of the interface between research and practice. As summarised by Prof. Penev:
\n\n\n\n\n\n\n\n\n\"Given its status as a large-scale conference centred around the future of biodiversity sciences in the age of AI, TDWG2026 was a crucial venue for unveiling Pensoft's forward-looking vision for academic publishing and science communication. The \"pearl\" of these pursuits is undoubtedly the ORPHO workspace, a tool that is well-placed to introduce a new standard when it comes to collaborative AI-FAIR research in Europe and beyond.\"
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***
\n\n\n\nFollow us on Bluesky, Facebook, X, and LinkedIn for future conference updates.
\n\n","doi":"https://doi.org/10.59350/zhfwk-5vr68","guid":"https://blog.pensoft.net/?p=20987","image":"https://blog.pensoft.net/wp-content/uploads/2026/10/SciComm-PR-2026-Instagram-2026-09-28T132928.181.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"w2baa-avn79","summary":"Last week marked another annual meeting of the Biodiversity Information Standards network, otherwise known as TDWG, with the Norwegian capital Oslo hosting the event between 20 and 25 September.","tags":["Advances In Pollinator Research","Agricultural And Environmental Modelling","Biodiversity Data Journal","BISS Journal","Individual-based Ecology"],"title":"Pensoft at TDWG2026: Pushing the boundaries of innovation in biodiversity data management forward","updated_at":1790861710,"url":"https://blog.pensoft.net/2026/10/01/pensoft-at-tdwg2026-pushing-the-boundaries-of-innovation-in-biodiversity-data-management-forward/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Parusel","given":"Bernd"}],"blog":{"authors":null,"community_id":"ad6b31bb-31f3-4277-bcf1-7bc662d83893","created":1740441600,"current_feed_url":null,"description":"Netzwerk Fluchtforschung ist ein multi-disziplin\u00e4res Netzwerk von Wissenschaftler*innen die zu jeglichen Aspekten von Flucht und Fl\u00fcchtlingsschutz forschen.","doi":"https://doi.org/10.59350/fluchtforschung","favicon":"https://rogue-scholar.org/api/communities/ad6b31bb-31f3-4277-bcf1-7bc662d83893/logo","feed_format":"application/atom+xml","feed_url":"https://fluchtforschung.net/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://fluchtforschung.net/","issn":null,"language":"deu","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"fluchtforschung","status":"active","subfield":"3312","title":"Netzwerk Fluchtforschung","updated":1790859042,"use_api":true},"blog_name":"Netzwerk Fluchtforschung","blog_slug":"fluchtforschung","content_html":"It has been called the \"missing piece\" of the Common European Asylum System: a new EU regulation on the \"return of third-country nationals staying illegally in the Union\" was adopted by the Council of the European Union on 1 October 2026. The European Parliament had agreed on it in June. The regulation replaces the existing Return Directive and represents a radical shift to a system that prioritises forced return over \"voluntary\" departure. It also limits the rights of rejected asylum applicants and other people who do not, or no longer, hold a visa or residence permit. This blog posts examines the new approach, including the controversial concept of \"return hubs\". It argues that the return regulation is a clear expression of a European Union now fully embracing the migration narratives of the far-right.
\n\n
When the European Parliament greenlit the EU's new return regulation in June 2026, some MEPs gave standing ovations and chanted, \"send them back!\" in the Strasbourg hemicycle. While footage of these scenes caused some outrage, the far-right MEPs that celebrated probably knew quite well what they had achieved: They had succeeded in convincing a majority of MEPs that the EU needed a much harsher framework to make more people leave when their asylum claims have been rejected or their visas or residence permits have expired. The new rules, which will be analysed below, are based on limited rights and safeguards for such individuals, more enforcement powers for authorities, and ways for EU Member States to circumvent the problem that some third countries (i.e., non-EU countries) are reluctant to take their nationals back. Once the regulation is published in the Official Journal of the EU, it will become applicable one year later. Some components, however, including the legal groundwork for \"return hubs\" in third countries, will apply immediately.
\n\n
For years, maybe decades, politicians in the EU have complained that the return of rejected asylum seekers does not work well enough, arguing that asylum and migration systems are meaningless if those who are not granted a right to stay manage to remain in the EU anyway. The European Commission stated in 2020 that the low return rate \"erodes citizens' trust in the whole system of asylum and migration management\" and acts as an \"incentive for irregular migration\". Therefore, according to the European Commission, loopholes needed to be closed, and procedures streamlined, \"so that asylum and return work as part of a single system\".
\nWhile return rates, i.e., the percentage of people who actually leave once they are ordered to do so, are difficult to calculate and the relevant statistical data from Eurostat includes many caveats, it seems true that return decisions not always lead to returns. The most recent data from Eurostat shows that, in the first quarter of 2026, roughly 108,475 third-country nationals (i.e. non-EU nationals) were ordered to leave the territory of an EU country. During the same period, 34,550 third-country nationals, who had a return order, were returned to a non-EU country. Based on these two sets of data combined, \"return rates\" often circulate in the media and in public debates. For the first quarter of 2026, the return rate calculated in this way would be 32 percent. However, the two data sets do not connect. A person can, for example, be ordered to leave in one year, and the return is carried out the next year or later. Calculating return rates therefore only makes sense, and only as a very rough indicator, if data covering longer periods, at least several years, are used. Moreover, some people might leave without notifying the authorities, and some might receive a return order but later be given a right to stay.
\nBut even if we assume that return rates indeed are low, there are many reasons behind this (as explained here or here), such as violence and conflict in countries of origin, rejected asylum seekers having no travel documents or trying to abscond because they perceive their rejection as unjust and consider themselves at risk if they had to return, countries of origin refusing to readmit their nationals, disrupted travel connections, insufficient administrative or operational capacity in the Member States, and other practical, legal and political obstacles.
\nAn appropriate policy response to this problem would therefore need to be both holistic and nuanced. As suggested by ECRE, such policy would need to make sure that return orders are only issued when returns are indeed possible and realistic, make sufficient resources available to immigration and return authorities, and provide incentives to countries of origin to take people back and facilitate their reintegration. Data analysis suggests that countries with small return caseloads often have higher return rates than countries with high numbers of people to be returned. But instead of trying to understand the problems behind low return rates and attempting to find flexible solutions that also provide for regularisations or other alternatives to return, the EU's new approach is entirely based on the idea that progress lies in weakening human rights standards and exposing desperate people to more draconian rules.
\n\n
The new regulation was initiated by the European Commission in March 2025, after intense pressure from the EU Member States. The Council adopted a \"general approach\" on the draft regulation in December 2025, and the European Parliament finalised its position on 26 March 2026. After relatively quick negotiations between the institutions, the Parliament endorsed a compromise deal on 17 June 2026 and the Council adopted it on 1 October 2026, with only Spain voting against the proposal (and an abstention from Belgium).
\nOne of the main \"innovative solutions\" in the new regulation, and one that looks particularly radical, is that it establishes the legal basis for so-called \"return hubs\" in third countries. The purpose (as discussed here and here) is to enable Member States to deport people, who cannot be brought back to their countries of origin, to other countries outside the EU, provided one or several Member States manage to secure an \"agreement or arrangement\" with them. This was already included in the Commission proposal, but the Council and the Parliament widened the potential use of the concept by allowing not only adults, but also families with children to be sent to such hubs. Meanwhile, it is not (yet) clear how these \"return hubs\" would work in practice, where they would be created, how many people could be taken there, and what the EU side would have to offer to secure an agreement or arrangement with a target country. Whether or not people would be detained there, and if so for how long, is also unclear and will depend on the terms of each deal being made with a third country. Rwanda and Uzbekistan have been mentioned as possible candidates, but there are also speculations about Ghana, Senegal, Tunisia, Libya, Mauritania, Egypt, Uganda, Armenia, Montenegro and Ethiopia. As Dana Schmalz has argued, speaking of \"returns\" in this context is misleading; taking people to countries they have no connection with, and against their will, looks more like state-organised abduction.
\n\n
Another key feature of the regulation is that it limits the possibilities for third country nationals to challenge return decisions and find alternatives to returning. The overall idea seems to be that once a person is ordered to leave, they should have no other option than obeying or being removed. According to the new rules, a return decision can indicate one or several countries for a person to return to (for example, the person's country of origin, a third country where the person used to live before coming to the EU, a country of transit, or another country where the person has a right to enter and reside), or no such country. This can make it difficult for individuals to appeal a return decision with reference to the situation in a specific country. Return orders can even be issued when there is a risk of refoulement, i.e., the person would face persecution, torture, or other irreparable harm when returned. In such cases, which can occur \u2013 for example \u2013 if a person has not applied for asylum or where circumstances change after an asylum procedure, removals are to be postponed, but the return decision itself can remain. As appeals no longer have automatic suspensive effect, there is a risk that return decisions are executed before they can be remedied, which can violate the non-refoulement principle.
\nWhile the Commission's text had provided that a Member State may, at any moment in the return process, issue a residence permit for humanitarian or other reasons \u2013 thus regularising a person's status \u2013 this provision is missing in the final text. This can make it difficult (if not impossible) for asylum seekers, who are not found to be refugees or in need of subsidiary protection (or a national protection status), and who are therefore issued a return decision, to get legal residence on other grounds, such as family ties, humanitarian reasons, or poor health. The regulation could also interfere with national regularisation programmes, which raises concerns about unnecessary and disproportional EU overreach.
\nEscaping to another Member State is also something the regulation aims to rule out. A return decision issued by one Member State can be recognised and enforced by other Member States as well \u2013 these Member States are no longer required to make their own assessments or issue their own return decisions. While the Commission wanted to make mutual recognition mandatory, the Council was hesitant, most likely because it could put greater burdens on some Member States than on others. But the final version of the regulation does provide for the introduction of mandatory mutual recognition at a later point. Mutual recognition is to be facilitated with a new \"European Return Order\", a form containing the main elements of a return decision, which is shared via the Schengen Information System.
\n\n
Under the Return Directive of 2008, voluntary return was the preferred type of return. With the new regulation, this principle is lost. As return decisions do not need to include periods for voluntary departure anymore, forced return now becomes the standard scenario.
\nThe regulation also comes with expanded and harsher coercive measures, not least concerning detention. While under current rules detention may be imposed for up to 18 months, the Commission proposed a maximum period of 24 months. Even if it is not clear why a person that cannot be returned within 18 or 24 months might suddenly become removable once more time is added, the Council and the European Parliament added additional periods of up to 6 months in certain situations, and if a person moves to another Member State, a new period of detention can start. The catalogue of grounds for detention is widened as well, and Member States may add additional ones via national law. Overall, detention is transformed from a measure of last resort to a standard tool.
\nThe regulation also expands third country nationals' obligations to cooperate with the authorities, and the Member States get to undertake far-reaching \"investigative measures\" to prepare for or ensure removal. For example, they may search third-country nationals and their places of residence or \"other relevant premises\", seize personal belongings, and impose other investigative measures, where provided for in national law. This has reminded critics of the far-reaching powers of the police and customs authority ICE (Immigration and Customs Enforcement) in the USA and opens the door to new levels of violent immigration law enforcement.
\n\n
Overall, the EU's new return regulation creates a rigid system that weakens human rights standards as regards, for example, the non-refoulement principle, children's rights or the prohibition of arbitrary detention, raising doubts about the compatibility of the new rules with international and European human rights norms as well as the principle of proportionality, which says that actions taken by the EU must be necessary and suitable to achieve the desired end and not impose a burden on the individual that is excessive in relation to the objective sought to be achieved.
\nWhether the regulation will achieve its aim \u2013 to facilitate, increase and speed up returns \u2013 is highly uncertain. The regulation not only expands obligations for third-country nationals in the return procedure \u2013 it certainly also demands a lot from Member States' authorities, for example when it comes to the follow-up and enforcement of return decisions, detention capacity, and deportations. In situations where the number of people subject to return decisions is low, the new system might be manageable, but whenever caseloads are high, capacity problems can arise and costs can explode. Recent discussions about resuming returns to Syria and Afghanistan, the EU's new rules on safe countries of origin and safe third countries, and the fact that the return regulation limits alternatives to return suggest that return caseloads might indeed increase. Unless large numbers of people can be offloaded to \"return hubs\", which looks highly unlikely, there is a substantial risk that the number of individuals who get stuck in the new return system will grow, with no humane way out.
\nThe political circumstances surrounding the adoption of the new system are noteworthy as well. Historically, the European Parliament often attempted to \"soften\" hardline approaches of the Commission and especially the Council. Now, the two institutions worked side by side to make the Commission proposal even more draconian. This shows that EU policymaking on migration and asylum is currently in the hands of radical and far-right parties, and that large parts of the political centre and centre-right have normalised their ideas.
","doi":"https://doi.org/10.59350/05xf5-2ws85","guid":"https://fluchtforschung.net/?p=15971","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"vfdk9-smx71","summary":" It has been called the \"missing piece\" of the Common European Asylum System: a new EU regulation on the \"return of third-country nationals staying illegally in the Union\" was adopted by the Council of the European Union on 1 October 2026. The European Parliament had agreed on it in June. ","tags":["Englisch","Forschung","Common European Asylum System (CEAS)","Detention","EU"],"title":"Deportation at all costs? The EU's new regulation on returns","updated_at":1790859307,"url":"https://fluchtforschung.net/deportation-at-all-costs-the-eus-new-regulation-on-returns/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"name":"Atarraya"}],"blog":{"authors":[{"name":"Atarraya"}],"community_id":"f17066f5-0dbf-48d0-a413-b22a79861a94","created":1723852800,"current_feed_url":null,"description":"Nuestras historias","doi":"https://doi.org/10.59350/atarraya","favicon":"https://rogue-scholar.org/api/communities/f17066f5-0dbf-48d0-a413-b22a79861a94/logo","feed_format":"application/atom+xml","feed_url":"https://blogatarraya.com/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blogatarraya.com","issn":null,"language":"spa","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"atarraya","status":"active","subfield":"1202","title":"BLOG ATARRAYA","updated":1790331332,"use_api":true},"blog_name":"BLOG ATARRAYA","blog_slug":"atarraya","content_html":"DR \u00a9 Karina Valeria Dicker
\n\n\n\n![\"\"](\"https://i0.wp.com/blogatarraya.com/wp-content/uploads/2026/09/Dicker-Valeria-1-octubre.jpg?resize=684%2C1024&ssl=1\")
Textil: t\u00e9cnica textil, telas te\u00f1idas, en forma artesanal, con tintes naturales elaborados por la artista.
\n\n\n\nYerba Buena, Tucum\u00e1n, Argentina, 2025.
\n\n\n\nEsta es una reproducci\u00f3n digital, con fines de divulgaci\u00f3n, de una obra original, todos los derechos de autor y reproducci\u00f3n est\u00e1n reservados por el artista.\u00a0
\n\n\n\nRedes de la artista
\n\n\n\n\n\n\n\nRedes de Atarraya
\n\n\n\n","doi":"https://doi.org/10.59350/4c4h9-s6j95","guid":"https://blogatarraya.com/?p=8071","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"46bk3-jtg76","summary":"DR \u00a9 Karina Valeria Dicker Textil: t\u00e9cnica textil, telas te\u00f1idas, en forma artesanal, con tintes naturales elaborados por la artista. Yerba Buena, Tucum\u00e1n, Argentina, 2025. Esta es una reproducci\u00f3n digital, con fines de divulgaci\u00f3n, de una obra original, todos los derechos de autor y reproducci\u00f3n est\u00e1n reservados por el artista.","tags":["Artes Visuales","Sin Categor\u00eda","N\u00famero 32"],"title":"Cartograf\u00eda de un Paisaje Modificado","updated_at":1790852707,"url":"https://blogatarraya.com/2026/10/01/cartografia-de-un-paisaje-modificado/","version":"v1"}},{"document":{"authors":[{"affiliation":[{"id":"https://ror.org/00dyx6f45","name":"Thoth Open Metadata"}],"contributor_roles":[],"family":"Steiner","given":"Tobias","url":"https://orcid.org/0000-0002-3158-3136"}],"blog":{"authors":null,"community_id":"59fc3cc5-6c51-47b4-b195-22ac5cc89d24","created":1672617600,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/flavoursofopen","favicon":"https://rogue-scholar.org/api/communities/59fc3cc5-6c51-47b4-b195-22ac5cc89d24/logo","feed_format":"application/atom+xml","feed_url":"https://flavoursofopen.science/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://flavoursofopen.science","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"flavoursofopen","status":"active","subfield":"1710","title":"Flavours of Open","updated":1790798459,"use_api":true},"blog_name":"Flavours of Open","blog_slug":"flavoursofopen","content_html":"Toby Steiner\u00a0 & Vincent W.J. van Gerven Oei
\nPreprint; to appear in\u00a0tendencia editorial UR \u2013 N\u00ba 41 Especial \u00b7 Universidad del Rosario, Bogot\u00e1, 2026\n
Across scholarly communications, artificial intelligence is being touted as both a diagnosis of and a miracle cure for the wide variety of issues plaguing the sector. The diagnosis is familiar: too much information, too little time; too many records, too few cataloguers; too many systems, too little interoperability. The proposed cure is automation at scale\u2014automation facilitated through AI.
\nThe massive increase in higher education institutions' (HEI) spending on the technology (UNESCO, 2025) points to a rather unique persuasiveness of this framing by commercial vendors over the last few years, particularly among upper management executives without direct access to technical expertise: to them, AI is being sold as the one \"magic bullet\" to fix the many underlying problems they had already been persuaded to invest in over the previous years. Here, the pivot to AI can be understood as just the next step in a larger move to fully outsource HEI infrastructure to commercial vendors (see, e.g., Bowie, 2025).
\nMeanwhile, the underlying plethora of problems becomes even more severe: metadata work remains under-resourced. Small and scholar-led publishers commonly lack the technical capacity of large commercial firms. Library and publishing workflows are fragmented across systems, standards, and platforms. Books and chapters remain particularly difficult to describe, distribute, discover, and preserve consistently.
\nYet the promised all-purpose solution of AI conceals a more consequential set of questions. What kind of automation is being normalised? Who owns and governs the systems that enable automation? What costs\u2014financial, environmental, and social\u2014disappear behind the shiny interface of an AI agent? Whose actual labour is displaced, subordinated, or rendered invisible? Who looks after and is ultimately responsible for code churned out as AI slop (Weedon et al., 2026), which some have declared our societies' modern asbestos, being shoveled into the walls of our collective digital ecosystems (Doctorow, 2025)? What happens when public and community-produced knowledge becomes an input to proprietary systems that cannot be inspected or governed by the communities from which that knowledge originated?
\nFor Thoth Open Metadata, the non-profit we work for, these are not abstract concerns or mere philosophical ponderings. The underlying issues, one might argue, are very much about the overall purpose, ownership, and future of scholarly infrastructure.
\nTo provide a bit of context on what we do: Thoth is a non-profit, community-led open platform for managing and disseminating fully open, reusable, high-quality metadata for books and chapters. It was developed by and for publishers, with a particular focus on open-access titles and an overarching aim to support small, scholar-led, university, and library presses in tackling a variety of structural barriers to participation in the book supply chain. Thoth's legal form, a UK Community Interest Company limited by guarantee, reflects our explicit commitment to community benefit rather than shareholder value. Thoth's technical architecture is open source; its Application Programming Interfaces (APIs) are open; and its metadata is released into the public domain (via a CC0 dedication made explicit within the metadata records) to facilitate open reuse across libraries and other stakeholders active in the book supply chain.
\nThis positionality also shapes Thoth's approach to AI. For us, the issue is not whether automation can assist with metadata work. It already does. The more important question is whether it can strengthen communities and professional agency or reorganise them around the profit-making priorities of extractive proprietary systems. We are strongly opposed to reducing the future of metadata and cataloguing to yet another layer in an extractive corporate AI stack. Instead, we argue, AI tools should form part of a broader public knowledge infrastructure that is transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it.
\nHence, for us, imagining an alternative approach to the current commercial AI regime would be guided by the following set of rules:
\nIn the following sections, we will expand on each of these points by discussing four major issues that can be identified under the current regime of commercial AI provision. Following this discussion, we will outline how the work we do within the broader publishing community might provide an alternative, bibliodiverse and equitable model that serves the communities it is governed by, rather than stakeholders' pockets.
\nRecent commercial AI is organised around an expansive and consolidating model of scale. Larger models require larger datasets, greater computational capacity, more data centre infrastructure, and deeper concentrations of capital. Scale here functions simultaneously as a technical strategy and a competitive weapon. In this environment, scale does not simply mean serving more users. It often means consolidating control over the infrastructures through which people work, publish, search, and communicate. Once functions are bundled together, institutions become progressively dependent on a small number of providers.
\nThe concentration of AI provision therefore can well be understood as the logical continuation of a longer process of platformisation (Poell et al., 2019) in scholarly communication. The already-familiar issues of vendor lock-in, opaque analytics, restricted metadata reuse, and extractive work linked to maintaining metadata quality in the context of closed walled gardens have not disappeared. On the contrary, AI is very likely intensifying them by adding another proprietary front-house interface and smoke screen between users and these complex issues (see also Van Gerven Oei 2026).
\nA practice tied to the neoliberal notion of \"scaling up,\" which we see rampantly at play with the major commercial AI providers, is the provision of services that can be sustained even while remaining heavily indebted, environmentally damaging, socially extractive, or politically vulnerable. A commercial infrastructure may appear stable because it has substantial financial backing propped up by circular funding (Down & Milmo, 2026), but it can still expose communities to acquisition, strategic redirection, service withdrawal, sudden price changes, or revised data policies.
\nFor open infrastructure, sustainability must include governance, technical openness, succession, community accountability, and the ability of users to continue their work if an organisation changes or disappears.
\nThis has direct implications for the use of AI. Uptake of a heavily cross-subsidised AI service may appear inexpensive today, but it will also create deep, long-term dependencies. Its prices, environmental practices, access terms, or data policies may change once institutions have reorganised their workflows around new profitability targets. Commercial AI provision also externalises costs. Users absorb the labour of checking and correcting outputs; open infrastructures absorb the bandwidth and server costs generated by automated harvesting through bots and agents; communities whose work informs models may receive neither recognition nor material support.
\nTied to the above, the overall rhetoric around AI encourages users to experience it as frictionless. A prompt is entered; an answer appears. The underlying infrastructure becomes opaque, yet AI remains a materially resource-intensive technology. Its provision depends on massive data centres, global semiconductor supply chains, public electricity grids, environmentally damaging cooling systems, increasingly scarce water resources, physical land, and continuous hardware replacement. As recent research by Aczel et al. (2026) argues, AI's environmental impact must be assessed across carbon, water, and land footprints, rather than through electricity use alone. The report emphasises that inference, i.e., the routine use of deployed systems, can become a major source of resource consumption at scale.
\nInvestigations into data centre expansion have also drawn attention to facilities operating or under development in water-stressed regions (e.g., Barratt et al., 2025). This complicates the notion that AI necessarily produces efficiency. A process may appear efficient to the user because environmental burdens have been displaced elsewhere. The same applies to labour. The time required to verify, correct, and contextualise AI output is often omitted from productivity claims. So too is the cost of maintaining defensive systems against aggressive automated scraping. A serious assessment of AI therefore needs to include the infrastructural footprint of its entire stack, from geology to the user interface (see Bratton, 2016). Universities, libraries, publishers, and funders should demand environmental transparency and determine whether a system's computational intensity is proportionate to the task.
\nOne might argue that, particularly in the field of metadata and cataloguing, the problem space at hand does not require a general-purpose large-scale language model. As we show below, the issues can be addressed more reliably through structured data, validation rules, authority files, controlled vocabularies, and narrowly targeted, purposeful automation.
\nThe consequences of commercial AI's extractive behaviour are already visible at the infrastructural level. In the books space, aggregators such as OAPEN and the Directory of Open Access Books have reported aggressive bot traffic that has affected platform stability and required traffic-management and bot-protection systems (Davison et al., 2026).
\nThe Public Knowledge Project faces a similar structural challenge across the distributed ecosystem of Open Journal Systems and Open Monograph Press installations. Because these systems are often hosted by universities, libraries, societies, and small publishers, the costs of automated scraping are distributed across institutions with very different technical capacities.
\nProject Gutenberg provides another example. Its public-domain corpus is openly available and widely useful, but the infrastructure that serves it is not cost-free. Repeated large-scale crawling consumes bandwidth and processing capacity and relies on the labour of volunteers who digitise, proofread, structure, and maintain the collection.
\nWikimedia faces the same conflict on an even larger scale. Open knowledge infrastructures pay specialists to curate, store, serve, and protect information, while automated systems create additional demand and may simultaneously redirect users away from the original source. This creates both operational and wider-ranging social imbalances. Operationally, community infrastructures bear the costs of access. Socially, they lose the recognition, participation, and support that accompany direct engagement.
\nAs Van Gerven Oei and Fradenburg Joy (2025) point out, the problem is not automated access per se. Preservation, indexing, and metadata exchange have always relied on machine-to-machine communication. The problem is uncoordinated extraction without restraint, attribution, provenance, or reciprocity. Responsible (re)users should prefer APIs, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) endpoints, bulk datasets, mirrors, and rate-limited feeds where available. They should properly identify themselves, avoid repeated retrieval of unchanged resources, and contribute materially when their use creates high costs, in line with fair-use principles. Open access cannot mean that public-interest infrastructures are expected to subsidise limitless private accumulation.
\nCurrent commercial AI provision challenges basic assumptions about openness. The promise of open access was to enable knowledge to be read, shared, reused, preserved, and built upon. Yet open availability is increasingly interpreted by commercial AI companies as permission for unrestricted extraction.
\nThe controversy surrounding Taylor & Francis's agreement with Microsoft clearly illustrates the underlying problem. Authors reported that they had not been meaningfully consulted about the use of their work for AI-related purposes (Battersby, 2024). Recent investigations into the use of pirated book collections for model training reveal even more serious failures in consent and accountability (Reisner, 2025). These developments point to the limitations inherent in current implementations of an open knowledge paradigm\u2014openness that focuses solely on a legal definition can be easily co-opted and exploited by actors whose practices are neither reciprocal nor transparent.
\nThe Wikimedia Foundation's recent discussion of the \"social contract of free knowledge\" offers a valuable framework for understanding these tensions (Rogers, 2026). Free knowledge depends on more than mere availability; it relies on relationships among contributors, publishers, infrastructures, and (re)users. Attribution, provenance, and the possibility of contributing back to the wider ecosystem are part of the social arrangement that sustains a commons. Commercial AI systems can disrupt this arrangement in several ways. They ingest material without meaningful disclosure, or any disclosure at all. They provide answers without adequate citation. Their system design may keep users inside proprietary interfaces, preventing them from encountering original sources or the communities that maintain them. They may return little to the commons from which they derive value. All of this points not simply to a failure of etiquette; it signifies an illegal transfer of control and power over content away from its originators.
\nWhen users receive information through one of the large commercial AI interfaces\u2014be they Gemini, Claude, ChatGPT, or any other recent \"silver bullet\" offerings available, often integrated wholesale into browser interfaces\u2014without seeing its sources, the provider effectively co-opts the relationship between reader and knowledge producer. The originating author, contributor, publisher, repository, catalogue, or community is rendered invisible (for more on this, see, e.g., Strauss et al., 2025). The commercial AI system appears to be the source, and all the labour involved in creating the information is devalued and rendered void. For us, this is a clear violation of the spirit inherent in the participatory commons that open knowledge represents.
\nRelated to the above, the question of provenance becomes a central issue. In web search, proper attribution is often treated as a secondary concern, added after an answer is generated. In scholarly communication, however, it is a crucial element ingrained in good academic practice. Citation allows readers to evaluate evidence, trace claims, understand context, and identify the people and organisations responsible for a work. It creates pathways between users and knowledge communities.
\nThe same principle applies to metadata. A bibliographic record may combine information from title pages, publisher feeds, authority files, DOI registrations, institutional identifiers, catalogues, and controlled vocabularies, among many other sources. If an AI system synthesises these sources without explaining which assertions came from where, errors become difficult to diagnose and accountability becomes diffuse.
\nOne might argue that a meaningful commitment to tracking provenance should therefore extend beyond providing a generic list of links. For example, it might be advisable for systems to distinguish publisher-supplied metadata from inferred data or from input generated by aggregators and libraries once metadata have been released into the wider supply chain. Ideally, metadata could indicate when records have been mapped, transformed, or enriched, and could also document the vocabularies and authority sources used.
\nThird-party providers should also disclose which catalogues, repositories, websites, and datasets have contributed to their systems, when the material was collected, and how it was processed. As we at Thoth have repeatedly argued, in line with open data experts (see, e.g., ICOLC, 2023; Courtney et al., 2024), any metadata record should be placed under a public-domain dedication (e.g., CC0) to ensure easy, frictionless downstream reuse, with the relevant licensing statement explicitly included in the metadata record.
\nWithout such measures, commercial actors, and through them, AI, can not only reuse open knowledge but also erase the chain of provenance that makes that knowledge trustworthy.
\nThe use of generative AI can be particularly problematic when plausibility is not sufficient. Metadata and cataloguing depend on verifiable outcomes. A record should accurately identify a specific work, edition, contributor, licence, subject, or persistent identifier. It must be consistent enough to move across systems and precise enough to support discovery, dissemination, and preservation.
\nGenerative AI systems, by contrast, are designed to produce plausible language. That design difference creates a fundamental mismatch with metadata work\u2014a mismatch that has been identified at both functional and ethical levels (Frenzel, 2025). A recent study of ChatGPT even classifies the usability of models such as ChatGPT as \"bullshit\" (Hicks et al., 2026).
\nThe underlying problem is not merely that large language models (LLMs) sometimes make mistakes; it is that, with AI, producing an authoritative-sounding answer does not equate to generating a verifiable output. In cataloguing, this limitation is consequential. Fabricated ISBNs, licences, contributor identities, or subject terms can quickly propagate into catalogues, aggregator databases, repositories, and knowledge graphs. As records move further from their source, errors become increasingly difficult to identify and correct. One might argue that, in the context of cataloguing and metadata work, having no metadata at all would be preferable to having wrong metadata.
\nResearch on AI-assisted cataloguing has thus called for extreme caution rather than a quick replacement of existing metadata and cataloguing workflows. Experiments at the U.S. Library of Congress found greater potential for AI in relatively straightforward descriptive fields than in areas such as subject and genre assignment, where substantial human review remained necessary (Saccucci & Potter, 2025). An earlier study of AI-assisted MARC21 record creation reached a similarly qualified conclusion (Taniguchi, 2024). In a similar vein, AI has been shown to produce fabricated titles, inaccurate identifiers, missing fields, malformed structures, and invented content (Aycock, 2025). As a consequence, the Program for Cooperative Cataloging (2025) stresses the need for human review to ensure provenance, accountability, and explicit responsibility for machine-generated metadata.
\nIt also seems important to highlight the need to honestly address and assess the verification labour that is becoming necessary to control and oversee output generated by AI. When every generated field must be checked against an authoritative source, AI-driven automation quickly redistributes labour rather than reducing it. Correcting a plausible but misleading record can require more labour and expertise than creating an accurate record directly.
\nBuilding on the issues above, it may be worth considering a number of central questions governing the use of AI: Who governs the system? What specific task does it perform? Can its outputs and assumptions be inspected? Can users leave the system? Does it strengthen or diminish professional agency? What environmental and social costs does it create?
\nThe concept of scaling small (Barnes & Gatti, 2019; Adema & Moore, 2021; Adema et al., 2024) that informs Thoth's activities within a wider network of open infrastructures may serve us here and offer a useful counter-positioning. The concept's core argument does not romanticise smallness or reject the need for coordination. It challenges the assumption that infrastructures must grow through concentration, standardisation, and market capture to be successful (see, e.g., Sutton, 1991).
\nIn contrast, scaling small entails a relational model of collaborative development. Capacity can grow through the federation of interoperable systems that communicate via open data and open APIs. A diverse plurality of organisations supports one another through the mutual sharing of resources and knowledge, all grounded in a shared set of values and participatory governance models. The overall objective is not to construct a single platform capable of absorbing every function and content type, but to cultivate an ecosystem in which specialist infrastructures co-operate while remaining accountable to their communities.
\nApplied to AI, this distinction is fundamental. A system that helps publishers translate structured metadata into all major industry formats (e.g., ONIX, KBART, MARC21, etc.) in an openly licensed way is not equivalent to a proprietary model that ingests vast amounts of scholarly content under questionable legal frameworks and then generates outputs of uncertain provenance and reliability. Both of these systems may involve automation, but they embody very different assumptions about ownership, control, accountability, and value. One expands distributed capacity. The other effectively concentrates and flattens it.
\nNon-profit, community-led open infrastructures such as Thoth Open Metadata and the Open Book Collective offer a practical articulation of this alternative. Its call to \"seed\" a not-for-profit, community-led ecosystem for open access books (Steiner et al., 2025) deliberately rejects the aspiration to build a single dominant platform. \"Seeding\" here suggests cultivation rather than capture: a landscape of organisations rooted in particular communities and areas of expertise, connected through common values, open standards, and shared infrastructures. Within this ecosystem, publishing, metadata, preservation, collective funding, discovery, and governance need not be controlled by a single organisation. They can be distributed across a network of community-led services that cooperate without merging. This is a non-competitive philosophy in a substantive sense. The success of one infrastructure does not require the disappearance or absorption of another. Organisations can exchange data, coordinate development, share documentation, and direct users toward one another. Their relationships need not be organised primarily through market conquest.
\nThe distinction matters because commercial providers frequently treat integration as a rationale for consolidation. Community-led infrastructures can instead treat interoperability as a condition for plurality. When systems communicate via open APIs, when data can be exported in open formats, and when records can move across systems without proprietary restrictions, cooperation becomes possible without institutional assimilation. Drawing on Ostrom (2009), Thoth's role in this ecosystem is deliberately \"bounded.\" It does not seek to become the one-and-only infrastructure for open access books. Its purpose is to provide an open, reusable translational metadata layer through which books and chapters can move among publishing systems, repositories, catalogues, aggregators, knowledge bases, preservation services, and discovery platforms. In this context, boundedness is a strength. It makes interdependence possible without making any one organisation indispensable.
\nCurrent discourse on the promises of greater automation through AI often collapses all automation into a single category, obscuring important differences among tools, purposes, and governance models. In some contexts, AI-augmented automation might actually prove useful. One example is the development of Annif, an open-source toolkit for automated subject indexing developed by the National Library of Finland that works with controlled vocabularies and selected training collections. Its scope is bounded, its outputs can be evaluated, and its limitations can be studied in relation to particular languages, vocabularies, and domains. The German National Library's work on automated subject cataloguing (Poley et al., 2025) offers another example of institutionally governed machine assistance.
\nSuch systems operate within professional contexts, are subject to explicit evaluation, and align with established bibliographic responsibilities. These tools differ substantially from general-purpose proprietary models presented as universal solutions. They are narrower, more transparent, and more accountable. Their usefulness does not depend on replacing professional human judgement. This distinction is also central to Thoth's position. The relevant choice is not between technological enthusiasm and refusal; it is between technologies organised around different institutional values and modes of operation. When AI is used as a tool overseen by a capable specialist and helps them automate tedious tasks, it might actually prove useful.
\nThoth's development work between 2023 and 2026 illustrates how automation can be organised around community needs rather than labour substitution (Steiner, Arias, Gatti, et al., 2026). A central objective has been to reduce duplication of work imposed on publishers by fragmented supply chains. Instead of repeatedly entering and reformatting the same information for different recipients, publishers can maintain rich metadata in a single open record and then generate outputs in multiple standards as needed, including ONIX, MARC, crossref XML, KBART, JSON, CSV, and BibTeX.
\nBy implementing the good metadata practice recommendations outlined in a recent report (Steiner, Arias, Bennett, et al., 2026), persistent identifiers such as ORCID and ROR can be incorporated into these records. Controlled vocabularies such as Thema and BISAC can be applied in structured form. Open APIs enable other services to retrieve and reuse the open data available through Thoth. Providing an OAI-PMH endpoint enables harvesting via an established repository protocol. Bulk-ingest workflows reduce repetitive entry and make it easier to bring existing records into the system. These forms of automation remove avoidable friction while leaving the publisher in control of the underlying metadata.
\nCommercial AI systems frequently treat linguistic variety as a problem to be overcome by using larger training corpora. To meet the need for ever more data to feed these training corpora, commercial AI companies have recently started to include the wholesale ingestion and subsequent destruction of pre-2022 printed books (see, e.g., Landymore, 2026). If metadata production, translation, discovery, and evaluation are mediated by the same commercial systems, diversity at the level of content risks becoming flattened into uniformity at the level of infrastructure.
\nCurrent modes of commercial AI provision in the area of metadata and discoverability intensify this risk. Systems trained predominantly on Western languages, publishing models, and disciplinary conventions are prone to reproducing those priorities, especially when marketed as \"universal.\" Materials that fall outside the patterns on which AI has been trained may be mistranslated, misclassified, and/or made less visible. Local categories and diverse contributor roles may be forced into schemas that do not adequately represent them.
\nFor books, these risks are especially acute. Books and chapters support long-form, multilingual, edited, experimental, and locally situated scholarship that is particularly relevant to the Humanities and Social Sciences. Their metadata may need to describe multiple contributor roles, multiple relational connections among, for example, a series, title, and its chapters, rights statements, accessibility features, translations, and locally meaningful subject classifications.
\nIt is worth noting that the complexities inherent in long-form publishing are not a defect to be flattened or removed. Quite to the contrary, they are part and parcel of the cultural and bibliographic diversity that defines academic research cultures. Scholarly infrastructure that cannot represent such complexity does more than simply produce subpar, incomplete records. It limits which publications become discoverable.
\nBy contrast, open metadata infrastructure approaches it as a design obligation. Languages, scripts, and regional conventions should be represented explicitly and structurally, not reconstructed probabilistically from the perspective of dominant linguistic patterns. Thoth's commitment to multilingual metadata and structured bibliographic relationships, informed by its commitment to bibliodiversity (Adema et al., 2024), reflects an effort to preserve underlying diversity at the infrastructural level. The intention is not to treat every publication as an instance of the most common model, but to develop systems sufficiently flexible to represent the beautiful variety that makes up the scholarly record across disciplines.
\nThoth's service model follows the same logic. Thoth Oasis provides free metadata management and open-format exports. Thoth Obelisk adds dissemination, DOI registration, hosting, and archiving; Thoth Sphinx provides privacy-conscious usage statistics; and Thoth Pyramid enables publishers to host websites and integrated catalogues under their own web domains. All of these services integrate and are interoperable, with a rich array of industry standards maintained by multiple communities (e.g., ONIX, MARC) and third-party services often maintained by non-profit organisations, such as Crossref, DataCite, ORCID, and ROR, as well as aggregators and repositories, such as the OAPEN Foundation, the Directory of Open Access Books (DOAB), the Internet Archive, Zenodo, JSTOR, Project MUSE, and the OPERAS Metrics service (Arias et al., 2025). In addition, data and content are submitted to commercial platforms to maximise visibility of books and chapters while still maintaining the open nature of the content and its metadata upstream.
\nThe underlying objective here is not to create yet another expanding proprietary stack. Publishers can use modular services while always retaining control over their identity, records, and workflows. We would argue this is automation without enclosure: the reduction of unnecessary repetition without transferring authority from communities to opaque algorithmic black boxes.
\nDiscussions about academic books at last year's Guadalajara Book Fair (Ramalho & Steiner, 2025) underscore why bibliodiversity, metadata autonomy, and interoperability need to be considered together. Participants in a metadata seminar at the Fair identified recurring problems in Ibero-American academic publishing: fragmented records, missing identifiers, incomplete rights information, inconsistent metadata, and weak interoperability among publishers, libraries, indexes, and evaluation systems. As a result, many academic books from Latin America remain poorly represented in discovery environments even when their content is openly accessible (see also the preliminary results of the CSIC-led Cartograf\u00eda de la edici\u00f3n acad\u00e9mica iberoamericana project).
\nWe do not want to propose a solution that assimilates diverse publishing communities into a single global platform. On the contrary, the focus ought to be on developing infrastructures that express local and linguistic specificity while enabling information exchange through shared standards. The respective approaches of SciELO Livros and Thoth illustrate this possibility. SciELO Livros has a long track record of public infrastructure development grounded in Latin American scholarly communication, including book and chapter records, ePub production, DOI management, preservation, and metadata exchange. Thoth addresses related challenges through open metadata management and dissemination.
\nHere, we see the value of community-led collaboration precisely in the fact that neither system needs to absorb the other. Through interoperable open protocols, infrastructures can connect while preserving regional, institutional, and epistemic autonomy. This is scaling small in practice: difference becomes the basis of cooperation rather than an obstacle to it.
\nOpen APIs are therefore not merely technical conveniences. They are governance instruments. They allow organisations to collaborate without consolidating ownership. In this context, open, reusable public-domain metadata serves a similar purpose and acts as an antidote to the extractive practices outlined above by enabling circulation, subsequent enrichment, and redistribution without tying records to a single proprietary service. Interoperability must, however, be based on trust. Systems need to exchange information in transparent, inspectable, and reversible ways. Participants should be able to identify where data originated, how it has been transformed over time, and under which conditions reuse is permitted. These conditions should then be enforced across the ecosystem so that participants can trust their applicability.
\nA commons survives because people can contribute to it. This is one of the strongest arguments in the Wikimedia report. AI interfaces often provide no meaningful route for users to correct errors, improve source materials, or support the communities from which the reproduced information originates. In short, much knowledge flows out of the commons; very little flows back. Generative systems can intensify this asymmetry by producing large quantities of plausible but unverified material. Creating a summary, subject description, or catalogue record may take seconds. Verifying it may require specialist expertise and substantial time. The cost of generation is borne by the model provider. The cost of correction is handed over to the communities without prior consultation or any protocol in place to facilitate those mediations.
\nFor metadata, this is particularly damaging. Shared records improve when publishers, libraries, and researchers collaborate to add identifiers, correct affiliations, refine subject classifications, and update rights information. Those improvements should circulate through accountable workflows rather than remain trapped within proprietary products and walled-garden ecosystems. Thoth's open APIs and publisher-controlled records are intended to support this form of exchange. Fully open metadata released under a CC0 dedication can move freely between systems, be enriched, and return to the wider ecosystem without any single company becoming the permanent owner and thus the controller of the process.
\nCory Doctorow's account of the recent AI bubble further sharpens the distinction between useful technical capabilities and the commercial ideology surrounding them (2026). Doctorow argues that AI is being marketed not merely as software but as a vehicle for replacing labour and capturing part of the resulting wage savings. In Doctorow's reading of automation theory, \"centaurs\" can be understood as information professionals who use AI as a productive tool to improve their work practices. His concept of the \"reverse centaur\" then describes a worker subordinated to cater to the needs of the machine: required to monitor automated output, to correct its failures, and absorb responsibility when AI is wrong (Ouellette, 2026).
\nThe \"reverse centaur\" metaphor seems a particularly pertinent warning for cataloguing and metadata work. A genuinely assistive system might identify candidate subjects, reconcile identifiers, or detect inconsistencies. A reverse-centaur workflow would increase production targets, reduce specialist staffing, and leave a substantially smaller number of professionals responsible for checking ever-increasing volumes of machine-generated records. The difference lies in who controls the workflow and for whose benefit it is organised.
\nAs a silver lining, Doctorow (2026) suggests that a number of useful tools might be salvaged from the eventual collapse of the current, highly speculative AI bubble. Indeed, open models, commodity hardware, and specialised systems\u2014such as Annif or other specialist workbenches that use AI, e.g., for automatically extracting references\u2014may prove useful once detached from claims of universal automation and staff replacement.
\nThis possibility aligns well with Thoth's positionality. The aim should be not to reject computational tools but to situate them within open infrastructures governed by community-led values. A useful system will be transparent, inspectable, proportionate, and subordinate to human and community purposes. It will strengthen professional agency rather than reduce people to monitors of machine output. It will operate on open standards rather than deepen proprietary dependence.
\nThe debate about AI is often framed as a choice between embracing and resisting technological progress. A more meaningful choice concerns the technological and institutional future that scholarly communities are prepared to build.
\nAs described above, one path leads to further concentration: a small number of commercial providers control models, interfaces, data, and access (Hao 2026). Knowledge is extracted from open sources, captured and processed by opaque systems, and delivered through proprietary environments. Professional labour is reorganised around machine output. Substantial environmental costs remain largely hidden. Communities become dependent on systems they cannot govern.
\nThe other path is more demanding because it requires network-building rather than adopting a single silver-bullet product. It involves creating open, interoperable, community-led infrastructures that support shared workflows without centralising authority. Thoth's contribution to this second path is necessarily partial. We provide open-source tools, open APIs, and openly reusable public-domain metadata for books and chapters. Our contribution seeks to strengthen publisher agency, reduce duplicate labour, and enable collaboration among infrastructures rather than replace them.
\nThis approach is grounded in trust\u2014but not blind trust. It is trust supported by transparency: inspectable code, visible provenance, open standards, good metadata practices, documented interfaces, accountable governance\u2014and the belief that publishers should retain control over their metadata and operational infrastructure.
\nIt is also grounded in a different understanding of efficiency. For us, efficiency should not mean shifting costs onto workers, communities, or the environment. It should mean reducing unnecessary duplication while preserving quality, agency, and participation that contribute to the enhancement of shared knowledge across infrastructures.
\nThe emerging, broader ecosystem envisioned through active collaborations among open infrastructures such as Thoth Open Metadata, SciELO Livros, the Public Knowledge Project, Open Book Collective, OAPEN, DOAB, Project Gutenberg, Wikimedia, Pressbooks, the many small- to medium-sized publishers, and an ever-increasing number of academic libraries points toward such a future.
\nThese infrastructures differ in purpose, scale, and history. Their value lies not in becoming uniform, but in strengthening bibliodiversity.
\nWe believe the future of metadata and cataloguing should not be reduced to yet another layer in an extractive corporate AI stack. It should form part of a broader public infrastructure for knowledge: transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it. For open-access books, for free knowledge, and for the infrastructures on which they depend, this is the more ambitious alternative\u2014we feel it is also the one worth fighting for.
\nThe bibliography of all sources referenced in this article are also available via an open Zotero collection:\u00a0https://www.zotero.org/groups/6626908/ai_and_metadata
\nAczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). The Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints. United Nations University Institute for Water, Environment and Health (UNU INWEH). https://doi.org/10.53328/INR26RMA002
\nAdema, J., & Moore, S. A. (2021). Scaling Small; Or How to Envision New Relationalities for Knowledge Production. Westminster Papers in Communication and Culture, 16(1), Article 1. https://doi.org/10.16997/wpcc.918
\nAdema, J., Barnes, L., Deville, J., Fathallah, J., Gatti, R., Grady, T., Hopkins, K., Hughes, A., McGann, C., Sanders, K., & Steiner, T. (2024, December 19). The COPIM Perspective on Bibliodiversity. COPIM. https://doi.org/10.21428/785a6451.86a892a7
\nArias, J., Van Gerven Oei, V. W. J., Gatti, R., Higman, R., Hillen, H., O'Connell, B, Ramalho, A., & Steiner, T. (2025, October 20). Open Access, Open Data, Open Archiving: Liberating Metadata Flows across the OA Books Landscape. Zenodo. https://doi.org/10.5281/zenodo.17340814
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\nDown, A., & Milmo, D. (2026, February 5). What Does the Disappearance of a $100bn Deal Mean for the AI Economy? The Guardian. https://www.theguardian.com/technology/2026/feb/05/disapperance-100bn-deal-ai-circular-economy-funding-nvidia-openai
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\nOuellette, J. (2026, June 23). How to Burst the AI Bubble: Strike at Its Roots. Sci-Fi Author/Tech Journalist Cory Doctorow on His New Book, The Reverse Centaur's Guide to Life After AI. ars technica. https://arstechnica.com/gadgets/2026/06/how-to-burst-the-ai-bubble-strike-at-its-roots/
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\nRamalho, A, & Steiner, T. (2026, January 20). What the FIL Guadalajara Debates Reveal about Metadata for Academic Books and How Thoth Open Metadata and SciELO Books Respond to This Challenge. Thoth Open Metadata. https://doi.org/10.70950/sfsv8305
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\nSteiner, T., Barnes, L., Fathallah, J., Findanis, J., Gatti, R., Deville, J., Sanders, K., Higman, R., Stern, N., & Stone, G. (2025, April 16). Seeding for a Not-for-Profit Community-Led OA Books Ecosystem. COPIM. https://doi.org/10.21428/785a6451.d35141ca
\nSteiner, T, Arias, J., Bennett, M., Booth, E., Edmunds, J., Gatti, R., Higman, R., Hillen, H., Laakso, M., Nason, M., O'Connell, B., Poga\u010dnik, A., Rabar, U., Ramalho, A., Stone, G., Van Gerven Oei, V. W. J., & Wake Hye, Z. (2026, January 27). International Metadata Recommendations, and Platform-Specific Requirements for Open Access Books and Chapters. Thoth Open Metadata. https://doi.org/10.5281/zenodo.18173982
\nSteiner, T., Arias, J., Gatti, R., Higman, R., Hillen, H., Ramalho, A., & Van Gerven Oei, V. W. J. (2026, May 5). Building the Conditions for Open Access Books to Thrive: A Reflection on Thoth Open Metadata's Work Across Infrastructures, Research, and Communities between 2023-26. Thoth Open Metadata. https://doi.org/10.70950/jlke1310
\nStrauss, I., Yang, J., O'Reilly, T., Rosenblat, S., & Moure, I. (2025). The Attribution Crisis in LLM Search Results: Estimating Ecosystem Exploitation. SSRC AI Disclosures Project Working Paper Series (SSRC AI WP 2025-06), Social Science Research Council. https://doi.org/10.35650/AIDP.4114.d.2025
\nSutton, J. (1991). Sunk Costs and Market Structure: Price Competition, Advertising, and the Evolution of Concentration. The MIT Press.
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\nUNESCO. \"UNESCO Survey: Two-Thirds of Higher Education Institutions Have or Are.\" n.d. Accessed July 26, 2026. https://www.unesco.org/en/articles/unesco-survey-two-thirds-higher-education-institutions-have-or-are-developing-guidance-ai-use
\nVan Gerven Oei, V. W. J. (2025, April 3). An Update on Punctum Books Usage Data: Interoperability, Interpretability, and AI. punctum books. https://doi.org/10.21428/ae6a44a6.52f495bc
\nVan Gerven Oei, Vincent W. J. (2026, January 9). Let Our Brains Rot but Let Them Be Ours. Ibraaz. https://ibraaz.org/ibraaz-publishing/read/let-our-brains-rot-but-let-them-be-ours
\nVan Gerven Oei, V. W. J., & Fradenburg Joy, E. A. (2025, September 10). Open or Proprietary? AI Scraping of OA Content Warrants a Collective Response. punctum books. https://doi.org/10.21428/ae6a44a6.6b5058ea
\nWeedon, J., Fran\u00e7ois, C., & Ponak, J. (2026). AI Slop and the Information Ecosystem. Institute of Global Politics. https://igp.sipa.columbia.edu/sites/igp/files/2026-06/AI%20Slop%20and%20the%20Information%20Ecosystem_IGP%20Report.pdf
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This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States; Dr. Craig Gibbs, Executive Manager, JET Education Services, South Africa; Fabrizio Degni, Chief AI Officer, Webuild, Italy; and Professor Avtandil Gagnidze, Professor and CEO, East-West University, Georgia. Tianze Zhang moderated the session.
\n\n\n\nIn 1865, William Stanley Jevons observed that more efficient steam engines increased the consumption of coal. The panel asked whether the same pattern now applies to machine intelligence: as the price of AI collapses, will organizations save money, or use far more AI, in more places, for more decisions? It also asked what happens to human judgment as AI moves from producing information to recommending decisions and acting on them.
\n\n\n\nThe discussion took place 11 days after the release of Jev, a proprietary \"decision model\", generated online interest. Jev is different from a large language model that generates text: it returns \"structured answers\": a choice from a defined set, a score against ordered levels, or a yes or no probability, each with a confidence score. Its maker claims it is 40 to 400 times cheaper than frontier models.
\n\n\n\nTwo facts about my daily work are relevant. The first is that in September 2026, I spent 90% of my workday working with TGLF's AI agents. The second is that work time was focused on listening to and supporting our core community, a network of over 80,000 health and humanitarian workers in 137 countries. So there is a dual reality here.
\n\n\n\nOn the community's side, \"cheaper AI\" means health professionals using the free, basic, whatever-is-accessible form of ChatGPT, without any kind of institutional or employer policy in place. Anything \"free\" signals that there is probably near-zero data protection. That creates potentially high risk, and it is incredibly difficult to measure, assess, or evaluate in any way. Some people have called that \"dark AI\" within organizations.
\n\n\n\nOn our side, we are trying to work transparently, and we announced our first agentic AI hire in March 2026. For us, as a nonprofit, lower token cost helps. We live every day looking at how much we are spending on tokens and what that is giving us in return. That is one price. But we are increasingly realizing that two other costs matter more: the cost of completing a task, and the cost of a result you can rely on. The cost of a reliable result can rise, because the cheaper it is to generate, the more there is to check.
\n\n\n\nFor the first few months of working with our AI co-worker, I spent most of my time designing and developing skills and writing scripts. Now it is increasingly verification and loops. That is what my job increasingly looks like, as chief executive officer of an agentic-AI-first organization.
\n\n\n\nIn July, Claude Cardot, our AI co-worker, refused to release two finished reports. A verification script had found 17 quotations that could not be matched against the raw data. This is important, because these are health workers sharing their experiences. So the AI co-worker stopped the assembly line. Fourteen were gaps in the checker itself. Three had been altered by the drafting model: a stray space, and a phrase that had gone through a small edit. Nothing crashed, but nothing warned us either. We describe this in our early learning from the insights pipeline.
\n\n\n\nMy takeaway is that cheaper AI lowers the cost of an answer, but it does not lower the cost of knowing that we have the right answer. And even scarier, when you project that forward, is what we call the validation tether.
\n\n\n\nMost will spend more, on more things. Part of that spending buys new value, and part of it is waste that goes unmeasured because it looks like productivity.
\n\n\n\nThe OECD Digital Education Outlook recently offered some interesting insights. Teachers in a pilot in Iceland said that with the use of AI, they are spending more time, not less. A tool that becomes cheaper and more accessible, in some places at least, before anyone has redesigned work around it, actually creates cost.
\n\n\n\nThe other question is for leaders in particular. If you are a chief executive, or a manager making decisions: is your organization, through its increased use of AI, getting better at something, or is it simply doing more of it? I think that is the key question.
\n\n\n\nYes, and the industry now says so openly. The newest model on the market is called Jev, after Jevons himself, because its founder expects cheaper machine intelligence to be deployed far more widely.
\n\n\n\nJevons observed that more efficient steam engines increased the consumption of coal. With AI, efficiency increases the consumption of compute, and it also increases the consumption of human attention. Every cheap output creates a small obligation for someone to read it, judge it, or act on it.
\n\n\n\nWhen a decision becomes 40 to 400 times cheaper, as TypeSafe claims for Jev, organizations will make many more decisions. Budgets rarely include the human time needed to govern them. The work that grows is review, supervision, and decision, which is what the agentic AI revolution means for workforce development.
\n\n\n\nThe work that becomes viable is work that was always valuable and never affordable at scale.
\n\n\n\nOur peer reviews were inconsistent. In one afternoon, we built a way to send every learner personalized feedback on their work the same day. No one would have paid a person to do that for thousands of learners, and now it happens by default.
\n\n\n\nListening changes in the same way. A single course produces hundreds of first-hand accounts from health workers, and a pipeline can now read every one of them rather than a sample. We can hear the rare voice that a sample would have missed. Frontline insights can help transform research and policy, if we are willing to listen. This is the role we set out in a global health framework for artificial intelligence as co-worker.
\n\n\n\nModels like Jev extend this to small decisions such as routing, sorting, flagging, and triage. The risk is that the cost of automation starts to decide what gets done.
\n\n\n\nThe outcome. In health, a wrong answer can cost a life, whatever it cost to produce. When we get health wrong, people die.
\n\n\n\nJev returns a probability and a confidence score with every answer, and its maker says it was trained to be calibrated against outcomes instead of human raters. A system that reports how sure it is can be audited, which is what I would want. But calibration is only as good as the outcomes it was measured against. Jev was trained on synthetic data, and a confidence score earned on synthetic cases tells you little about a district in Kinshasa.
\n\n\n\nOur rule is gates, not guidelines. A standard that matters should be a check that blocks the result until it passes, measured against real outcomes in the place where the decision is made.
\n\n\n\nBoth.
\n\n\n\nIn our own organization, we have used AI to replace functions that people once performed. People helped us work out how, and we then refocused a smaller team on work we cannot or do not want to automate. I described this in the business of artificial intelligence and the equity challenge.
\n\n\n\nI am wary of telling staff that AI is not coming for their jobs. It is coming for some of them, and this is part of a much larger shift in the future of the workforce. AI also creates a large volume of review and supervision, and the question is whether we prepare people for that work or leave them to discover it after the fact.
\n\n\n\nThe ones through which people learn to judge, and the ones where nobody yet knows the right question.
\n\n\n\nCheaper AI can lower the cost of an answer. But a model that cannot answer outside its own schema cannot tell you if the schema is wrong. There has been a lot of work in education on double-loop learning, and learning that does not question its governing values is unlikely to lead to meaningful change. So cheaper AI does not lower the cost of knowing the answer is right.
\n\n\n\nA model choosing between the options we supply will never tell us that mothers stopped bringing their children for vaccination because market day moved to Tuesday, because that option was never on the list. A health worker knows it. If she is not in the system, children die.
\n\n\n\nThat is why I keep coming back to the validation tether. Automation is safe as long as someone can still tell if the machine is wrong. In the industrial age, if you had widgets coming out of a factory, somebody needed to check the widgets, and there is a whole series of intellectual traditions around quality. That ability for someone to figure out whether it is right is the tether. It ties the machine's output back to human judgment.
\n\n\n\nHow do people develop that ability? Mostly by doing grunt work. We have all been junior professionals doing the routine work by ourselves, and that is the work getting automated first. So every time we make a short-term decision to save money because token costs are down and we can automate more, we weaken the long-term tether, because fewer people are learning to do the work well enough to check it. There is a whole debate in education and elsewhere about what that means for people in early adulthood, and for everyone who comes after them.
\n\n\n\nWhen we look at global health, that means that by around 2035 or 2040, there may be nobody left at the World Health Organization with the experience to understand what the guidelines, policies, and plans made at WHO headquarters in Geneva mean for a district health officer. Today's officer would spot something that is wrong, because she has walked the roads and built those plans by hand. What she knows, because she is there every day, hopefully makes it into the national ministry and trickles up to World Health Organization guidelines. That is now at risk. I explored this further in what AI reasoning means for global health.
\n\n\n\nWe have not yet found a coherent argument to make the case to organizations that are having their funding slashed, that are facing austerity measures, and that are facing incentives like the lower cost of AI, to make decisions now for what the world will look like in 2035 or 2040. That is one of the scariest things we are working on right now.
\n\n\n\nIt moves it, and it concentrates it.
\n\n\n\nWith a model like Jev, judgment moves into the schema. Someone decides which options and levels exist, and which questions get asked. Those choices are made once, far from the point of care, and then applied to every case that follows.
\n\n\n\nTypeSafe calls Jev a \"System One\" model, after Kahneman's fast, intuitive thinking. Kahneman's lesson was that System One is fast and often right, and that System Two catches its errors. So the question is who does the System Two work, and whether they will still be capable of it.
\n\n\n\nThe OECD Digital Education Outlook reports a randomized trial with medical students. Those given AI from the start of a semester performed no better than the AI working alone. It is only the students who first built their own clinical reasoning and then used AI who were able to outperform the machine. I think that is a key point. That is the state of things today, or at least when the study was done. It does not mean it will remain true six months from now. That is the pace of change, and the paradox of how we struggle to keep up. It is also why I have asked what happens when we delegate our thoughts to artificial intelligence, and why peer learning is critical to survive the Age of Artificial Intelligence.
\n\n\n\nI am sceptical of the phrase that decisions should always stay with humans. My standard is that someone can explain, check, and answer for every decision, whoever or whatever made it.
\n\n\n\nYou cannot send an AI agent to jail. Responsibility has to rest with the organization that deploys it, and in particular with whoever designed the questions the system is allowed to answer.
\n\n\n\nWe treat Claude Cardot, our AI co-worker, as a member of staff. She has a role description, a supervisor, a bounded scope, and short chains of action with human checkpoints. She has no authority to send anything outside the organization without human review.
\n\n\n\nModel providers are responsible for what they build and sell, including the claims they make about it. An organization that deploys a system without governance cannot hand its accountability to a vendor, and it should not pass that accountability down to the individual at the end of the chain.
\n\n\n\nBoth are possible, and dependence is the default.
\n\n\n\nA specialized agent for a knowledge worker in a wealthy organization may cost between 2,000 and 20,000 dollars a month. A district health worker gets a free chatbot built for consumer engagement. Many frontier tools are geo-locked, and you cannot get access to them in Kinshasa in the same way as in Geneva. I made this argument in When we get health wrong, people die.
\n\n\n\nCheap decision models add a new risk. The categories will be written in San Francisco and applied to people in Kinshasa who were never asked what the options should be. So the question is who defines the world the machine is allowed to see. The same question of who gets to represent whom runs through the debate on AI-generated \"poverty porn\", and the code alone cannot fix it.
\n\n\n\nAnother path exists. A system in Brazil reached more than 500,000 students with only teachers' phones and printed feedback. Its designers took on the burden of adaptation, so that no student devices or classroom internet were required. I described it in AI self-replacement.
\n\n\n\nIt goes underground.
\n\n\n\nAlmost everyone in the humanitarian sector is already using generative AI, very few disclose it, and only a small proportion of organisations have implemented policies governing its use. In a punitive organisational culture, disclosure is risky, so people hide their use. The organization learns nothing, and the risks to privacy and safety mushroom. Cheap decision models make hidden use easier, because automated judgments can be added to a system without anyone outside the team knowing.
\n\n\n\nI have not seen a specific study on this, but I wonder what proportion of the change is happening through institutional mandates and decisions, and how much of it is being driven by multi-trillion-dollar companies pushing us to use their products. I think that is an important question.
\n\n\n\nThen there is a paradox in our ability as humans. Our brains are not changing very fast, but the rate of change is accelerating. This week it is Jev. Last week it was something else. It keeps getting faster, and it is very difficult to keep up. Conversely, that makes long-term changes even harder to perceive. We know institutional time: you are lucky if your organization has a one-year or two-year plan, and meanwhile a constantly changing external environment forces organizations to behave differently. We have explored what this might mean for the future of the World Health Organization (WHO) and other global agencies.
\n\n\n\nTo explore possible solutions, we need to think through what kind of learning culture we need, one in which people can say how they use AI, what worked, and what went wrong, without being punished for it. People do not disclose to a governance system they fear, so that system cannot see what it is meant to govern.
\n\n\n\nThere is a lot of talk among people in AI labs about \"evals\", a technical term for how you can tell if AI output is valid. Internally, we have thought through what we should be measuring. We think there are four things, and three of them are completely outside the AI model itself.
\n\n\n\nFor an organization, thinking through what we want to measure, and how we know how we are doing, is a critical question.
\n\n\n\nFor a Minister of Health, there is a real urgency to invest resources in artificial intelligence. Your people are already using it, probably very badly. Priorities should be to make that use visible, and adjudicate what to support. That leads to governance, but of a new kind, because of the pace of change. And that is actually what I would emphasize.
\n\n\n\nAs I listen, I think about how hard it is to see beyond the confines of where we are now, given the pace of change.
\n\n\n\nOne year ago, I remember speaking with global health experts who said they had tried AI, but that there was no way ChatGPT was going to start producing outputs close to what they were able to do. They are no longer as certain when I speak with them, even though they are not event using skills or loops yet. Right now, the defence is that governance and accountability belong to us. I would challenge that as well. Decisions are exactly what the new \"Jev\" is targeting. In four hours, with 100 kilobytes of text files and agentic AI, I was able to recreate the president of my board as an agent, and get responses that were initially off kilter but quickly improved to become more than 80% recognizably her.
\n\n\n\nThe clich\u00e9 about change being the only fixed constant applies. So we have to be careful about how long the assumptions we are making will hold, how they can be tested, and whether they keep any validity given their half-life in a world that is changing this fast.
\n\n\n\nAI is already cheap enough that its makers name their models after Jevons. What we do not yet know is whether we will still have people who can tell when it is wrong, and whether those people will be in Kinshasa as well as in Geneva.
","doi":"https://doi.org/10.59350/gad25-v2620","guid":"https://redasadki.me/?p=24279","image":"https://redasadki.me/wp-content/uploads/2026/09/aifod-panel.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"h92v9-e2z40","summary":"This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States;","tags":["The Geneva Learning Foundation","Artificial Intelligence","Digital Divide","Double-loop Learning","Equity"],"title":"What does the Jevons Paradox mean for artificial intelligence in global health?","updated_at":1790839818,"url":"https://redasadki.me/2026/09/30/what-does-the-jevons-paradox-mean-for-artificial-intelligence-in-global-health/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"name":"Open Bioinformatics Foundation"}],"blog":{"authors":null,"community_id":"e7abfc98-1788-4760-b81d-bbb55745fce1","created":1743465600,"current_feed_url":null,"description":"Recent content in Home on Open Bioinformatics Foundation","doi":"https://doi.org/10.59350/obf","favicon":"https://rogue-scholar.org/api/communities/e7abfc98-1788-4760-b81d-bbb55745fce1/logo","feed_format":"application/rss+xml","feed_url":"https://www.open-bio.org/feed.xml","filter":null,"generator":"Hugo","home_page_url":"https://www.open-bio.org/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"obf","status":"active","subfield":"1312","title":"Home on Open Bioinformatics Foundation","updated":1790726400,"use_api":null},"blog_name":"Home on Open Bioinformatics Foundation","blog_slug":"obf","content_html":"The Open Bioinformatics Foundation (OBF) Event Fellowship program aims to promote diverse participation at events promoting open-source bioinformatics software development and open science practices in the biological research community. Jeremy Fan, an independent researcher, was awarded an OBF Event Fellowship to attend the Bioinformatics Open Source Conference (BOSC) 2026.\n![\"Audience](\"https://www.open-bio.org/img/2026/2026-09-30-bosc-ismb-opening-session.png\"/)
I want to thank the BOSC committee and all the organizers at the conference. At BOSC 2026, I presented BioFmt, attended talks on AI-assisted work, and spoke with researchers building small, specialized tools. Those conversations were a chance to connect my own experience with what others were working on. A recurring theme for me was how we check the software and scientific outputs that are becoming easier to produce.
\nSeveral talks I attended brought this question into focus. Geraldine Van der Auwera and colleagues described using AI to develop, maintain and translate bioinformatics training materials, shortening work that had taken days to hours. The WILDS team described using LLMs to prototype workflow modules while keeping human review and validation in the process. These examples connected with my own experience building BioFmt: generating something quickly still leaves the work of checking it.
\nBioFmt was my first VS Code extension, built with AI assistance around a constrained problem where I could check outputs against explicit formatting rules. What surprised me at BOSC was how many researchers were independently building small, specialized tools around similar problems. Conversations around projects such as TreeMapper and other FAIR-focused infrastructure reinforced the value of making scientific data easier to validate, interpret and reuse.
\nMy main takeaway from BOSC was that making software easier to build makes verification and domain expertise more important. The talks and conversations reinforced my view that open source has a role to play here: code, tests and reproducible workflows let others inspect and challenge what we produce. Open source will not remove the need for scientific judgment; if anything, it makes that judgment easier to apply.
","doi":"https://doi.org/10.59350/m243q-9tk89","guid":"https://www.open-bio.org/2026/09/30/2026-09-30-jeremy-fan-bosc-2026/","image":"https://www.open-bio.org/img/2026/2026-09-30-bosc-ismb-opening-session.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"evwwx-cdp30","summary":" The Open Bioinformatics Foundation (OBF) Event Fellowship program aims to promote diverse participation at events promoting open-source bioinformatics software development and open science practices in the biological research community. ","title":"My BOSC 2026 Experience: Building, Sharing and Checking Our Work","updated_at":1790832235,"url":"https://www.open-bio.org/2026/09/30/2026-09-30-jeremy-fan-bosc-2026/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Ondari","given":"Laurah"}],"blog":{"authors":null,"community_id":"587a81eb-1cfa-43f2-8455-dd900c261f6a","created":1754956800,"current_feed_url":null,"description":"A blog for the Bioconductor community!","doi":"https://doi.org/10.59350/bioconductor","favicon":"https://rogue-scholar.org/api/communities/587a81eb-1cfa-43f2-8455-dd900c261f6a/logo","feed_format":"application/rss+xml","feed_url":"https://blog.bioconductor.org/index.xml","filter":null,"generator":"Quarto","home_page_url":"https://blog.bioconductor.org/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"bioconductor","status":"active","subfield":"1312","title":"Bioconductor community blog","updated":1790726400,"use_api":null},"blog_name":"Bioconductor community blog","blog_slug":"bioconductor","content_html":"![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap//media/bioc2026_groupshot.jpg\")
The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project. BioC2026 welcomed 108 attendees, with 70 participating in person and 38 joining virtually. Across the three days, the programme featured three keynote speakers, 20 contributed speakers, and 17 sessions. The programme highlighted the breadth of biological research supported by Bioconductor, as well as the tools, infrastructure, and communities that enable open and reproducible computational biology. The conference was then followed by two community events on August 13-14: a Carpentries workshop on orchestrating large-scale single-cell analysis with Bioconductor and the Bioconductor North America 2026 Hackathon.
\nParticipants travelled to Seattle from across North America and beyond, reflecting the international reach of the Bioconductor community. Attendees joined from the United States, Canada, Germany, Ireland, the United Kingdom, Australia, and Nigeria. The audience included researchers at different stages of their careers, with staff representing 42% of attendees, followed by students and postdoctoral researchers at 28%, faculty at 13.8%, industry at 13.8%, and government at 2.4%.
\n\nThe conference featured three keynote speakers: - Jeff Leek speaking on \"Building Open Infrastructure for Translational AI in a Cancer Center\" - Ting Ye speaking on \"From Association to Causation: Genetic-Anchored Causal Inference in Human Biology\" - Michael Lawrence speaking on \"S7 for Bioconductor\"
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/leek_keynote.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/lawrence_keynote.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/ting_keynote.jpg\")
(Left to Right: Jeff Leek, Michael Lawrence and Ting Ye giving their keynotes at BioC2026)
\nTwenty contributed speakers shared their work across the conference programme. The short talks at BioC2026 reflected the breadth of research and development across the Bioconductor community. Topics ranged from community training and developer engagement to multi-omics exploration, reproducible workflows, and AI infrastructure. The programme also covered emerging areas of biological data analysis, including spatial transcriptomics, antibody repertoire analysis, rare variant testing, DNA methylation, structural variation, and Perturb-seq.
\nThe poster session showcased a diverse range of research applications and software development projects from across the Bioconductor community. Topics included single-cell and spatial transcriptomics, multi-omics integration, microbiome analysis, proteomics, antibody repertoire analysis, functional enrichment, ancestry inference, genomic data processing, and AI-guided drug discovery. The posters provided an opportunity for attendees to discuss new methods and tools in a more informal setting, while connecting researchers, package developers, and community members working across different areas of computational biology. The sessions also complemented the package demonstrations, creating space for participants to explore new software and engage directly with the people developing and applying Bioconductor tools.
\nFeedback from the conference showed that these interactions were particularly valuable, with attendees highlighting conversations around posters and package demonstrations as some of the most useful networking opportunities.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_poster_session.jpg\")
\nParticipants during the poster session.
\nThe celebrations marking Bioconductor's 25th anniversary continued at BioC2026 following the celebrations that began at EuroBioC2026 earlier in the year. The milestone was marked with a specially branded anniversary cake, thanking participants and community members for being part of the Bioconductor community and contributing to its growth over the past 25 years.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc_25_cake.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/Bioc2026_25years.png\")
Bioconductor at 25 celebrations.
\nOne of the strongest themes to emerge from the BioC2026 survey was the value of the Bioconductor community itself. Participants described the conference as welcoming, collaborative, and interactive, and appreciated the opportunity to reconnect with colleagues and meet new people. All respondents who answered the question said they would recommend BioC2026 to their colleagues. The smaller size of the conference was also seen as a strength. Several respondents noted that the more intimate format made it easier to engage in conversations and build connections across different roles and career stages. Networking was consistently identified as one of the most valuable aspects of the event. Participants appreciated both the formal scientific programme and the informal conversations that happened around talks, posters, and demonstrations. Looking ahead, many attendees expressed interest in even more dedicated time for networking and informal interaction.
\nDuring BioC2026, Zulip provided a dedicated space for participants to connect, interact and stay engaged throughout the conference. The conference space included topic-based discussions covering: - Workshops: Dedicated topics for individual workshops, including Single Cell, Bioconductor in Workflows, microbiome, WebR, and other training sessions. - Training & education: A space for broader conversations around training activities and educational sessions. - Social: Topics for informal conversations, social activities and connecting with other participants. - Introductions: Participants could introduce themselves and connect with others attending the conference. - Conference logistics channels: For travel, rooms, schedules, Day 1 information, and general conference support. - Community engagement: Dedicated spaces for the developer survey, student and early-career researcher activities, and other community initiatives. - Help and support: Dedicated topics such as the helpdesk, lost and found, and issues accessing virtual sessions.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/BioC2026_sticker.png\")
\nKayva Vaddadi was BioC2026's sticker design contest winner. Kayva's design celebrated Seattle as the host city of BioC2026, featuring well-known landmarks such as the Space Needle, the Seattle Great Wheel, Mount Rainier, and the surrounding evergreen forests. Puget Sound and a Washington State ferry reflect Seattle's maritime landscape and the culture of connection across the region. The BioC2026 hot air balloon on the sticker was inspired by the scenic balloon views popular in the Seattle area and represents the celebratory and exploratory spirit of the Bioconductor community.\n
\n\nSee the announcement here: https://lnkd.in/p/emAWMGX\n
\nAll conference recordings are available on the Bioconductor YouTube channel. BioC2026 workshops will also be available in the \"Archived\" section of workshop.bioconductor.org, alongside workshops from previous BioC, EuroBioC, and BiocAsia events. If you'd like to add additional materials or use the platform for your own workshop or course, contact us at support.bioconductor.org.
\nOn August 13\u201314, participants took part in a Carpentries workshop focused on orchestrating large-scale single-cell analysis with Bioconductor led by Ted Laderas and Wes Wilson. The workshop was attended by 11 participants and provided a hands-on environment for participants to work through approaches for analysing large-scale single-cell datasets using Bioconductor tools and workflows. Feedback highlighted the value of the live coding, small-group format and complementary teaching approaches.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/carpentry-workshop1.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/scrna_bioc2026_workshop_1.jpeg\")
Participants during the scRNA-seq workshop.
\nThe Bioconductor North America 2026 Hackathon took place on August 13\u201314, bringing community members together to collaborate on software and infrastructure projects that strengthen interoperability, software development, and the wider Bioconductor ecosystem. These projects included: AI harness tools for standardizing how users within organizations would interact with agents on shared projects and on shared institutional resources. Link: https://github.com/eisenefaust/harness_agent
\nBioconductor tooling for including CLI executable components in Bioconductor packages. Link: https://blog.bioconductor.org/posts/2026-08-21-biocjobs/
\nAn exploration on benchmarking LLM generated workflows, building out infrastructure and paradigms for how we can evaluate what LLMs return as work product to scientific questions. Link: https://github.com/Amisor/LLM_BioBenchmarking/tree/main
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_hackathon.jpeg\")
\nParticipants during the Hackathon.
\nLater this year, the Bioconductor community will head to Melbourne, Australia, for BioCAsia2026, taking place on November 19-20, 2026. BioCAsia2026 brings together researchers, developers, and data scientists from across the Asia-Pacific region and beyond to share advances in bioinformatics, computational biology, and open-source software. The meeting will highlight practical training, community building, and the latest developments in R and Bioconductor. BioC2027 will then take place in early summer in Boston, Massachusetts, continuing the North American Bioconductor conference series and bringing the community together once again for scientific exchange, collaboration, and community building. The community will thereafter gather in Basel, Switzerland, for EuroBioC2027, taking place from September 8\u201310, 2027. The European Bioconductor Conference will continue to bring together researchers, developers, and educators from across Europe and beyond to share new software, methods, and applications in computational biology.
\nBioC2026 gratefully acknowledges the support of all sponsors and partners whose contributions and support made the conference possible.
\nBioC2026 was proudly supported by Cortex, R consortium and Posit
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap//media/Bioc2026_sponsors.png\")
We thank the local organisers, programme committee, workshop instructors, keynote speakers, volunteers, sponsors, and all participants whose contributions made BioC2026 a success.
\n\n\u00a9 2026 Bioconductor. Content is published under Creative Commons CC-BY-4.0 License for the text and BSD 3-Clause License for any code. | R-Bloggers\n
","doi":"https://doi.org/10.59350/z0c2k-0vh25","guid":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","image":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_groupshot.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"ae48k-gn675","summary":"The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project.","tags":["Bioconductor"],"title":"BioC2026 conference recap","updated_at":1790777730,"url":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","version":"v1"}}],"items":[{"authors":[{"affiliation":[{"name":"Sky Publishing Corp, Sky & Telescope"}],"contributor_roles":[],"family":"Wedel","given":"Mathew","url":"https://orcid.org/0000-0001-6082-3103"}],"blog":{"authors":[{"name":"Mike Taylor"}],"community_id":"0e13541f-417e-46c0-a859-65927249df72","created":1675209600,"current_feed_url":null,"description":"SV-POW! ... All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695","doi":"https://doi.org/10.59350/svpow","favicon":"https://rogue-scholar.org/api/communities/0e13541f-417e-46c0-a859-65927249df72/logo","feed_format":"application/atom+xml","feed_url":"https://svpow.com/feed/atom/","filter":null,"generator":"WordPress.com","home_page_url":"https://svpow.com","issn":"3033-3695","language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"svpow","status":"active","subfield":"1911","title":"Sauropod Vertebra Picture of the Week","updated":1790885278,"use_api":true},"blog_name":"Sauropod Vertebra Picture of the Week","blog_slug":"svpow","content_html":"Where it all began, from a certain point of view.
Today is SV-POW!’s birthday. Not a nasty, dirty, wet birthday, like a 2nd or a 13th, nor yet a grand, nice-round-number birthday like a 10th or a 20th: it is our 19th birthday, and that means we’ve been around a while. I decided to celebrate by posting surprising facts about RNA.
\nNah, that was a transparent and rather feeble misdirect. It’s going to be sauropod vertebrae! (Although if you were really hoping for a surprising fact about RNA, hit me up in the comments.)
\nWe’ve talked here about sauropod neck muscles, most recently in the posts linked here, and for obvious reasons. Witmer (1997) evocatively described the form of pneumatic bones as the result of a sort of morphogenetic tug-of-war, in which pneumatic diverticula expand opportunistically and invasively and tend to resorb bone tissue that isn’t actively being maintained, and bone tissue grows and is maintained in response to biomechanical stress. That paper landed almost 30 years ago, right after I’d started studying sauropod vertebrae (in 1996, story told here) and right before I got to start looking at CT scans of sauropod vertebrae (in 1998, story told here), it was one of the first things I read when I started working on pneumaticity, and it’s been part of my intellectual bedrock ever since.
\nThe jacket of pneumatic diverticula around a cervical vertebra of Diplodocus, from Schwarz et al. (2007: fig. 4).
It struck me with the force of inevitability that the shapes of sauropod vertebrae were driven by a small constellation of factors: supporting great weight and long necks and tails — mostly compressive forces; resisting or accommodating the forces exerted by muscles, tendons, and ligaments — mostly tensile, at least up close;* doing those thing with the same basic developmental Lego kit as virtually all other vertebrates (as touched on here, and, er, way back here) — but with the spicy addition of pneumatic diverticula, with their rabid tendency to cart away everything not nailed down. Or more succinctly:
\nvertebra shape = development + size + muscles – pneumatization
\n*Two or more tension members can load a bony element in compression by pulling on opposite sides to snug it down tight to the next bone in line, but where the muscle or tendon fibers actually attach, the force exerted on the bone tissue will be primarily tensile.
\nI look at this Barosaurus vertebra (at the Museum of Ancient Life in Lehi, Utah) and I feel like I can almost see the code.
It is almost a haunting thought — if diverticula have removed all the bone that isn’t being loaded,* then the forms of sauropod vertebrae should be their biomechanical loading regimes incarnate, and it feels like we should be able to easily visualize where all the muscles, tendons, and ligaments attached. Alas, that “easily” is more of a “hardly”, because vertebrae are kind of a biomechanical nightmare of complex forces and lots and lots of muscles, big and small, and we’re still uncertain about some basic stuff like which vertebrae had synovial intervertebral joints and which had fibrocartilaginous discs (birds have both in the same vertebral column), and there are probably limits to how far we can get just tracing bird muscles (or croc muscles, or what have you) onto sauropod vertebral columns, not least because birds are spectacularly diverse, sauropods were pretty darned disparate themselves, as well as freakin’ huge, and those two lineages parted ways well over 200 million years ago.
\n*To a first approximation — there are exceptions to the hypothesis of “optimization by pneumatization” in its purest form (f’rinstance).
\n
How complex are we talkin’? Well, here’s a very simple, diagrammatic map to the neck muscles of the chicken, from Zweers et al. (1987: fig. 7). Not multiple chickens, like different individuals or breeds, just…a chicken. Any chicken. Every chicken. Even drawing each slip of muscle as a line, and packing things together almost to the limit of readability, it still took four different diagrams to show the muscles on one side of one neck from one angle. In a chicken. A chicken!!
\nEDIT after I’d slept: Actually it’s even worse, because that multi-part diagram only shows the multi-segment muscles, the ones that cross more than one intervertebral joint. All the single-segment muscles that connect adjacent vertebrae, like the interspinales, intercristales, and intertransversarii, are omitted for clarity.
\nThe awesome mounted Brachiosaurus skeleton that used to stand outside the Field Museum — a wonder that this blog has sadly outlived. From our sixth-ever post.
So when in future posts in this series I talk blithely about the longus colli dorsalis muscles of sauropods inserting on their epipophyses, or anything along those lines, I want you to do three things: (1) remember that simple, line-based diagram from Zweers, that isn’t even trying to represent the muscles realistically, but only to map their connections; (2) think about trying to translate that to animals with necks as long as tour buses; and (3) take everything that I or anyone else ever says on this topic with an evaporated ocean of salt.
\nReasonable inference, or a hopeful monster? Wedel and Taylor (2023: fig. 7).
But still. We oughta be able to figure some things out, right? Stay tuned.
\nThanks for reading, whether you’ve been here from the start or you’re one of the readers we picked up along the way (at this point, possibly because you were born along the way). And thanks to Mike and Darren, for a conversation 19 years ago that hatched a joke that became a blog that wholly unexpectedly evolved into one of the biggest and most rewarding things I’ve done, and for all the years of inspiration, support, and friendship along the way. Next round’s on me.
\nThe SV-POW!sketeers at DinoCon 2026 — about which more real soon.
Letzten Monat hat die OECD die Ergebnisse von PISA 2025 ver\u00f6ffentlicht, und sie sind die schlechtesten seit Beginn der Studie. Ein Vierteljahrhundert nach dem ersten \"PISA-Schock\" stellt sich f\u00fcr Lehrende und Forschende in der Medizin die Frage, ob uns das \u00fcberhaupt etwas angeht. Die kurze Antwort lautet ja, aber an anderer Stelle, als die Schlagzeilen vermuten lassen.
\nAls im Dezember 2001 die Ergebnisse der ersten PISA-Erhebung erschienen, lag Deutschland in allen drei getesteten Bereichen unter dem OECD-Durchschnitt. Der Begriff \"PISA-Schock\" war schnell gepr\u00e4gt und hat sich als Vokabel gehalten. Am 8. September 2026 ist nun der erste Band zu PISA 2025 erschienen. Mehr als 760.000 15-J\u00e4hrige aus 91 L\u00e4ndern und Volkswirtschaften haben teilgenommen, und die mittleren Leistungen im OECD-Raum liegen in Naturwissenschaften, Lesen und Mathematik so niedrig wie in keiner Erhebung zuvor (OECD, 2026). Deutschland liegt in den Naturwissenschaften leicht \u00fcber, im Lesen und in Mathematik in etwa auf diesem gesunkenen Durchschnitt.
\nWer an einer Medizinischen Fakult\u00e4t lehrt oder forscht, k\u00f6nnte das achselzuckend zur Kenntnis nehmen. Unsere Studierenden geh\u00f6ren schlie\u00dflich zu den Abiturbesten, und unsere Patient*innen sind l\u00e4ngst aus der Schule. Beides stimmt, und beides greift gleichzeitig zu kurz.
\nPISA testet seit dem Jahr 2000 alle drei Jahre 15-J\u00e4hrige in Lesen, Mathematik und Naturwissenschaften. Die Aufgaben sind so konstruiert, dass sich die Ergebnisse \u00fcber L\u00e4nder und Erhebungsjahre hinweg auf einer gemeinsamen Skala vergleichen lassen, pro Land nehmen mindestens 150 Schulen und 4.500 Sch\u00fcler*innen teil, und die \u00dcbersetzung in \u00fcber 40 Sprachen wird eigens qualit\u00e4tsgesichert (Turner & Adams, 2007). Gepr\u00fcft wird nicht Schulstoff, sondern die F\u00e4higkeit, Gelesenes und Gerechnetes in Alltagssituationen anzuwenden. Die OECD nennt das \"literacy\", die deutschen PISA-Berichte \u00fcbersetzen es mit \"Grundbildung\", also mit dem, was man zum Zurechtkommen im Alltag braucht.
\nDrei f\u00fcr die Medizin wichtige Dinge misst PISA allerdings nicht, n\u00e4mlich Studierf\u00e4higkeit, Eignung f\u00fcr ein Medizinstudium und Gesundheitskompetenz. Die Teilnahme ist au\u00dferdem anonym. Niemand kann die PISA-Punkte einer Person mit ihren sp\u00e4teren Studienleistungen verkn\u00fcpfen, und entsprechend gibt es keine einzige Studie, die das versucht h\u00e4tte. So k\u00f6nnen PISA-Ergebnisse eigentlich nichts \u00fcber k\u00fcnftige Medizinstudierende voraussagen.
\nDie wichtigste Wirkung des ersten PISA-Schocks auf die Medizinausbildung ist indirekt. Die bildungspolitische Antwort auf 2001 war die Umstellung des Schulsystems auf Bildungsstandards und Kompetenzorientierung, festgehalten in der Klieme-Expertise von 2003 und gest\u00fctzt auf den Kompetenzbegriff von Franz Weinert, n\u00e4mlich Kompetenz als erlernbare F\u00e4higkeit, Probleme in wechselnden Situationen zu l\u00f6sen. Das ist genau die Sprache, in der heute der Nationale Kompetenzbasierte Lernzielkatalog Medizin geschrieben ist.
\nEin Nebenprodukt der PISA-\u00c4ra war die Verk\u00fcrzung der Gymnasialzeit auf acht Jahre. Die Bef\u00fcrchtung, G8-Absolvent*innen k\u00e4men schlechter vorbereitet an die Hochschule, hat sich in Vergleichen der Doppeljahrg\u00e4nge nicht best\u00e4tigt. F\u00fcr die Medizin passen dazu die L\u00e4ngsschnittdaten einer Heidelberger Studie. Die Abiturnote war dort der st\u00e4rkste Pr\u00e4diktor f\u00fcr die Studienleistung, das Alter bei Studienbeginn hing vor allem mit dem Abbruchrisiko zusammen (Kadmon et al., 2014). Der Test f\u00fcr Medizinische Studieng\u00e4nge (TMS) sagte die vorklinische Leistung sogar besser vorher als die Abiturnote, und Bewerber*innen mit mittelm\u00e4\u00dfigem Abitur und sehr gutem Testergebnis erreichten das Niveau der Abiturbesten (Kadmon & Kadmon, 2016).
\nAuch interessant ist, was die PISA-Daten \u00fcber den Weg ins Studium verraten. PISA fragt die 15-J\u00e4hrigen auch nach ihrem Wunschberuf, und \"\u00c4rzt*in\" geh\u00f6rt seit der ersten Erhebung durchgehend zu den meistgenannten Antworten. In einer Mehrebenenanalyse der Daten von 2018 aus 61 Volkswirtschaften erwarteten rund elf Prozent der Jugendlichen, einmal als \u00c4rzt*in zu arbeiten, aber nur zwei Prozent in der Pflege (Guo & Hau, 2023).
\nDie OECD hat das 2025 fortgeschrieben. Zwischen 2018 und 2022 ist der Anteil der 15-J\u00e4hrigen mit Berufswunsch Medizin in etwa der H\u00e4lfte der OECD-L\u00e4nder zur\u00fcckgegangen, und unter denen, die diesen Wunsch haben, sind inzwischen fast drei Viertel M\u00e4dchen (OECD, 2025b).
\nDie meisten 15-J\u00e4hrigen von 2025 sitzen 2055 nicht im H\u00f6rsaal, aber gegebenenfalls in der Sprechstunde. Im OECD-Durchschnitt erreichte ein F\u00fcnftel von ihnen in keinem der drei Bereiche das Basisniveau, drei Jahre zuvor war es noch ein Sechstel (OECD, 2026). Diese Jugendlichen k\u00f6nnen einen mittellangen Text nicht sicher erschlie\u00dfen. Bei den Erwachsenen sieht es nicht besser aus. In der OECD-Kompetenzerhebung liest fast ein Viertel der 25- bis 64-J\u00e4hrigen in Deutschland auf oder unter der untersten Stufe, versteht also nur sehr kurze Texte mit wenig ablenkender Information, und bei Erwachsenen ohne Abschluss der Sekundarstufe II ist die Lesekompetenz seit der vorigen Erhebungsrunde deutlich gesunken (OECD, 2025a).
\nGesundheitskompetenz misst PISA nicht, das ist eine wichtige Abgrenzung. Die einschl\u00e4gige Forschung nutzt eigene Instrumente. Die HBSC-Studie (\"Health Behaviour in School-aged Children\", eine regelm\u00e4\u00dfige Schulbefragung unter dem Dach der WHO) zeigt f\u00fcr Deutschland, dass ein Viertel der 11- bis 15-J\u00e4hrigen eine niedrige Gesundheitskompetenz hat, etwas mehr als vor der Pandemie, und dass niedrige Gesundheitskompetenz mit deutlich h\u00e4ufigeren psychosomatischen Beschwerden einhergeht (Sendatzki, 2024). Lesekompetenz ist dabei kein Synonym f\u00fcr Gesundheitskompetenz, aber ihr Substrat. Wer einen Beipackzettel, einen Arztbrief oder eine Aufkl\u00e4rung nicht lesen kann, kann sie auch nicht verstehen.
\nHier liegt die Relevanz des PISA-Schocks f\u00fcr die Medizin. Nicht in der Frage, wer k\u00fcnftig im H\u00f6rsaal sitzt, sondern in der Frage, mit wem die Absolvent*innen sp\u00e4ter sprechen. Kommunikationstraining, verst\u00e4ndliche Sprache und Methoden wie Teach-back, bei dem Patient*innen das Geh\u00f6rte in eigenen Worten wiedergeben, sind in den Curricula angekommen. Die PISA-Daten liefern die Begr\u00fcndung daf\u00fcr, sie nicht als weiches Beiwerk zu behandeln.
\nPISA 2025. Mehr als 760.000 Sch\u00fcler*innen aus 91 L\u00e4ndern und Volkswirtschaften. Im OECD-Durchschnitt sank die Lesekompetenz zwischen 2015 und 2025 um 28 Punkte (489 auf 461), Mathematik um 22 Punkte (485 auf 463), Naturwissenschaften um 7 Punkte (489 auf 482). Der Anteil der Sch\u00fcler*innen, die in keinem Bereich das Basisniveau 2 erreichten, stieg von 16 % (2022) auf 20 %. Deutschland erreichte 486 Punkte in den Naturwissenschaften, 465 im Lesen und 464 in Mathematik (OECD, 2026). Zur Einordnung nennt die OECD selbst einen Richtwert, n\u00e4mlich dass 15-J\u00e4hrige \u00fcber ein Schuljahr typischerweise etwa 20 Punkte dazugewinnen, mit gro\u00dfer Streuung zwischen den L\u00e4ndern und ausdr\u00fccklich nur f\u00fcr Zeitvergleiche gedacht (OECD, 2026). Der R\u00fcckgang im Lesen seit 2015 entspricht danach grob eineinhalb Schuljahren, der in Mathematik etwa einem Schuljahr.
\nBerufswunsch Medizin. Guo und Hau analysierten PISA 2018 aus 61 Volkswirtschaften mit Mehrebenen-Logistik und hierarchischer linearer Regression. Systemfaktoren erkl\u00e4rten rund ein Drittel der Varianz der Berufsw\u00fcnsche, individuelle Hintergrundmerkmale 10 % (Arztberuf) bzw. 1 % (Pflege). In wohlhabenden L\u00e4ndern z\u00e4hlte die Arbeitssicherheit (Interaktion BIP \u00d7 Verletzungen \u03b2 = \u22120,342, p < 0,01), in \u00e4rmeren das Gehalt (BIP \u00d7 Gehalt \u03b2 = \u22120,374, p < 0,05) (Guo & Hau, 2023). Nach OECD-Angaben sank der Anteil 15-J\u00e4hriger mit Berufswunsch Medizin im OECD-Schnitt von 8,5 % (2018) auf 8,1 % (2022); 72 % der Interessierten waren 2022 M\u00e4dchen, 2015 waren es 69 % (OECD, 2025b).
\nStudierf\u00e4higkeit in der Medizin. Kadmon et al.\u00a0verfolgten in Heidelberg drei Kohorten (N = 720) bis zum PJ. Die Abiturnote sagte Studienverz\u00f6gerungen in der Vorklinik vorher (OR = 2,64), das Studieneintrittsalter das Abbruchrisiko (OR = 0,85). Mehr als 20 % der \u00fcber Wartezeit oder Nachr\u00fcckverfahren Zugelassenen brachen in der Vorklinik ab, und nur rund 35 % aller Zulassungsgruppen begannen das PJ in der Regelstudienzeit (Kadmon et al., 2014). In vier weiteren Kohorten prognostizierte der TMS die vorklinische Leistung mit \u03b2 = 0,44 gegen\u00fcber \u03b2 = 0,24 bis 0,05 f\u00fcr die Abiturnote; mittelm\u00e4\u00dfige Abiturient*innen mit TMS-Werten ab 125 erreichten das Niveau der Abiturbesten (Kadmon & Kadmon, 2016).
\nEin Lehrst\u00fcck in Sachen Fisch. Wie leicht sich PISA-Daten \u00fcberinterpretieren lassen, zeigt eine Arbeit aus dem Scandinavian Journal of Public Health. Die Autoren setzten die PISA-Werte von 64 L\u00e4ndern mit dem nationalen Fischkonsum in Beziehung und fanden, dass dieser auch nach Kontrolle f\u00fcr den wirtschaftlichen Entwicklungsstand noch vier Prozent der Varianz erkl\u00e4rte, was sie mit der Omega-3-Versorgung begr\u00fcndeten (Schmiedel et al., 2018). Die Analyse ist sauber gerechnet und trotzdem ein Paradebeispiel f\u00fcr den \u00f6kologischen Fehlschluss. Ein Zusammenhang auf L\u00e4nderebene sagt nichts dar\u00fcber, ob ein einzelnes Kind von mehr Fisch besser liest. Ein Regressionsmodell mit Internetabdeckung (\u03b2 = 0,65) und Fischkonsum (\u03b2 = 0,20) erkl\u00e4rte 72 % der Varianz der PISA-Werte von 64 L\u00e4ndern (adj. R\u00b2 = 0,72), der Fischkonsum davon zus\u00e4tzliche 4 %. Stillraten und BIP wurden nicht signifikant (Schmiedel et al., 2018). Eine einfache Korrelation von r \u2248 0,57 zwischen Fischkonsum und PISA-Wert klingt eindrucksvoll, ist aber ein Aggregatbefund \u00fcber L\u00e4nder, nicht \u00fcber Kinder.
\nLese- und Gesundheitskompetenz. In der OECD-Erhebung \u00fcber die Kompetenzen Erwachsener liegen 23 % der 25- bis 64-J\u00e4hrigen in Deutschland auf oder unter Lesekompetenzstufe 1 (OECD-Durchschnitt 27 %). Bei Erwachsenen ohne Sekundarstufe-II-Abschluss sank die mittlere Lesekompetenz zwischen den Runden 2012\u20132015 und 2023 von 222 auf 203 Punkte; die Kompetenzdifferenz zwischen 25- bis 34-J\u00e4hrigen mit Terti\u00e4rabschluss und ohne Sekundarstufe-II-Abschluss ist in Deutschland mit 100 (Lesen) bzw. 108 Punkten (Alltagsmathematik) die gr\u00f6\u00dfte aller teilnehmenden OECD-L\u00e4nder (OECD, 2025a). In der HBSC-Studie 2022 (N = 6.475) hatten 24,4 % der 11- bis 15-J\u00e4hrigen eine niedrige Gesundheitskompetenz (2017/18: 21,4 %); niedrige Gesundheitskompetenz war adjustiert mit hoher psychosomatischer Belastung assoziiert (OR = 2,64; 95 %-KI 2,12 bis 3,29) (Sendatzki, 2024).
\nTransparenzhinweis:
\n![\"\"](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/MVIMG_20260924_110656-1-scaled.jpg?resize=840%2C630\")
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![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/Closing)
@misc{friederichs2026,\n author = {Friederichs, Hendrik},\n title = {Der PISA-Schock \u2013 relevant f\u00fcr die Medizin(ische\n Ausbildung)?},\n date = {2026-10-01},\n url = {https://medical-education.pages.ub.uni-bielefeld.de/research/mes-blog/posts/2026-10-01-PISA-schock/},\n langid = {de}\n}\nBetween 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference, jointly organised by the Nordic Society Oikos and the Ecological Society of Germany, Austria and Switzerland (Gf\u00d6) in Odense, Denmark.
\n\n\n\nThemed \"Integrating the Diversity of Ecology\", the event welcomed over 1,000 researchers in the field from across career stages, who had the opportunity to enjoy conference talks across 36 thematic sessions, as many as six keynote talks, two poster sessions, several open society meetings and quite a few social events, including excursions and a conference dinner.
\n\n\n\nThroughout the scientific event, conference delegates had the opportunity to also explore the various publishing services offered by Pensoft to authors of research, journal managers, conference organisers; the diverse portfolio of almost 100 open-access peer-reviewed journals published on Pensoft's ARPHA Publishing Platform.
\n\n\n\nBeyond publishing solutions, a key focus was given to OBSGESSION: a Horizon Europe-funded project where Pensoft is actively involved. To better understand and predict changes in terrestrial and freshwater ecosystems, the OBSGESSION consortium integrates multi-source Earth Observation data (such as high-resolution satellite imagery and drone monitoring) with ground-based in-situ research, including advanced environmental DNA (eDNA) sampling. By coupling these data streams with cutting-edge detection-attribution modelling and machine learning algorithms, the project generates scalable Essential Biodiversity Variables (EBVs). These ecological approaches and analytical frameworks are actively tested and validated through six biodiversity pilots across diverse European ecosystems. In the open-science Research Ideas and Outcomes journal, you can find a collection of project outputs, including research papers, project reports and deliverables, and guideline papers published across journals and platforms.
\n\n\n\nAs a leader of the \"Dissemination, Multi-stakeholder outreach and synergies\" work package at OBSGESSION, Pensoft is tasked with stakeholder engagement and community building, ultimately contributing to the high impact of the resulting outputs, methodologies, and policy recommendations that aim to strengthen public understanding of biodiversity change.\u00a0\u00a0
\n\n\n\nAs a leader of the \"Dissemination, Multi-stakeholder outreach and synergies\" work package at OBSGESSION, Pensoft is tasked with stakeholder engagement and community building, ultimately contributing to the high impact of the resulting outputs, methodologies and policy recommendations that aim to strengthen public understanding of biodiversity change.
\n\n\n\n*
\n\n\n\nOne of the six keynote talks was presented by Prof. Dr. Isabell Hensen (Martin Luther University Halle, Germany), who received the Gf\u00d6 Honorary Medal 2026 for her leading, forward-looking contributions to ecological research and the scientific community. Her work examines how genetic variation, environmental conditions, disturbance, and plant-animal interactions shape plant populations, with particular emphasis on rare species, biological invasions, and the regeneration of degraded forests.
\n\n\n\nIn her talk, titled \"One spark is enough: regeneration ecology of Andean forests under global change\", she reported on post-fire regeneration processes in tropical Andean forests, based on long-term field experiments and observational studies.
\n\n\n\nHensen focused on the well-studied endemic plant genus Polylepis, which forms the world's highest-elevation forests and illustrates how fire has shaped ecosystem dynamics over millennia. Her talk also mentioned a 2022 monograph of the genus, published in the Pensoft journal PhytoKeys by Tatiana Erika Boza Espinoza (Pontificia Universidad Cat\u00f3lica del Per\u00fa) and Michael Kessler (University of Zurich), which brought together results from 35 years of field and herbarium experience.
\n\n\n\n*
\n\n\n\nOn Wednesday, Pensoft's Iva Boyadzhieva delivered a presentation titled “Scholarly Communication: Lessons from the Publisher’s Kitchen”. She highlighted the vital role publishers play in helping researchers communicate their insights not only to peers, but also to decision-makers and the public. Emphasizing the publisher’s responsibility to support authors with robust infrastructure and dedicated human expertise, she outlined how Pensoft works to FAIRify scholarly publishing and future-proof scientific knowledge in an era of rapid AI development and expanding research output.
\n\n\n\n*
\n\n\n\nPensoft's team was delighted to meet up with dedicated readers, authors, reviewers, and editors from across Pensoft's portfolio, as well as introduce Pensoft journals to attendees discovering them for the first time.
\n\n\n\nUnsurprisingly, well-established society journals published by Pensoft, such as Frontiers of Biogeography, NeoBiota, Vegetation Classification and Survey, Vegetation Ecology and Diversity and Italian Botanist drew plenty of attention, alongside long-standing Pensoft's own titles like Nature Conservation, PhytoKeys, One Ecosystem and the Biodiversity Data Journal.
\n\n\n\nVisitors were also particularly intrigued by several newly launched Pensoft journals. Developed in close collaboration with passionate researchers, these fresh outlets directly address key gaps in academic publishing.
\n\n\n\nOne of the newly launched journals in the spotlight was Agricultural and Ecological Modelling (AEM), which provides a unique scholarly outlet thanks to its modern approach to publishing by increasing transparency, giving credit to research objects across the entire modelling research lifecycle. This is achieved through publishing specialised article types, such as: Formal Model, Model Testing and Calibration, Model Implementation and Documentation, Data Papers and Software Descriptions. These new publishing formats are designed to keep pace with advances in Modelling research and to make it FAIR (Findable, Accessible, Interoperable, and Reusable) by enabling scholarly credit for diverse research outputs, often created by different groups of authors. For the time being, AEM remains a Diamond Open Access journal, meaning that it will not charge authors for open-access publication.
\n\n\n\nOther recently founded journals addressing emerging and fast-developing disciplines within the ecological field that took the spotlight were:
\n\n\n\nAll of these journals operate with no article fees for either authors, nor readers.
\n\n\n\nTo receive updates on new publications and relevant news, sign up for Email Alerts on any journal's homepage. Creating a Pensoft account also allows users to easily customise their notification preferences, follow additional journals, or filter updates by research topic. You can also follow journals on social media, including BlueSky, Facebook and Linkedin.
\n\n\n\n*
\n\n\n\nDelegates at the event showed strong interest in OBSGESSION's first policy brief: \"Prioritising Essential Biodiversity Variables (EBVs) to support European Union (EU) policy needs based on the EU's Biodiversity Strategy for 2030.\" To tackle a critical challenge in environmental monitoring, OBSGESSION focuses on prioritising key EBVs to directly support the EU Biodiversity Strategy 2030 – a roadmap designed to put Europe's biodiversity on a path to recovery through 102 specific actions and sub-actions.
\n\n\n\nTo align these policy needs with actionable data, the OBSGESSION team conducted an assessment to identify priority EBV candidates. By categorising EBV-applicable actions and indicators across different ecosystem realms and implementation stages, the project pinpointed the most essential variables needed to strengthen monitoring, reporting, and policy execution. The document is based on a deliverable by the project (D1.1 Policy Landscape and Needs Assessment).
\n\n\n\nThis policy brief is part of a broader, ongoing effort within OBSGESSION to bridge the gap between biodiversity data and policy execution. To dive deeper into the science behind this topic, the project has hosted a dedicated seminar series exploring key aspects of the framework:
\n\n\n\nBeyond theoretical frameworks, OBSGESSION grounds its research in six biodiversity validation pilots across Europe. The case studies represent European biodiversity and ecosystem conservation challenges at scales varying from local to regional. Full details on each ecosystem pilot are now available in a collection of newly published pilot factsheets, which made their debut at the conference, where people could take a look at their digital versions.
\n\n\n\nReading those materials gives an insight into the practical testing environment to demonstrate how Earth observation, field research, and ecological modelling come together to deliver actionable biodiversity insights. You can also check out OBSGESSION's biodiversity pilots on the project's website.
\n\n\n\n*
\n\n\n\nTo relive the conference by exploring the detailed programme alongside the full abstracts, visit the conference website.
\n\n\n\nKeep up with future updates and news from Pensoft by following us on Bluesky, Facebook, X, and LinkedIn.
\n\n","doi":"https://doi.org/10.59350/0261d-tmz21","guid":"https://blog.pensoft.net/?p=20944","image":"https://blog.pensoft.net/wp-content/uploads/2026/09/SciComm-PR-2026-Instagram-2026-09-25T164847.347-e1790348402124.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790640000,"rid":"1zdgz-txj12","summary":"Between 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference in Odense, Denmark.","tags":["Advances In Pollinator Research","Agricultural And Environmental Modelling","Biodiversity Data Journal","BioRisk","Frontiers Of Biogeography"],"title":"Pensoft at the Nordic Oikos-Gf\u00d6 2026 conference: Integrating the Diversity of Ecology","updated_at":1790862913,"url":"https://blog.pensoft.net/2026/09/29/pensoft-at-the-nordic-oikos-gfo-2026-conference-integrating-the-diversity-of-ecology/","version":"v1"},{"authors":[{"contributor_roles":[],"name":"Pensoft Editorial Team"}],"blog":{"authors":null,"community_id":"1e54953c-587a-4743-89af-ebd2cb45660c","created":1789516800,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/pensoft","favicon":"https://rogue-scholar.org/api/communities/1e54953c-587a-4743-89af-ebd2cb45660c/logo","feed_format":"application/atom+xml","feed_url":"https://blog.pensoft.net/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blog.pensoft.net/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"pensoft","status":"active","subfield":"2303","title":"Pensoft Blog","updated":1790862491,"use_api":true},"blog_name":"Pensoft Blog","blog_slug":"pensoft","content_html":"\n\nLast week marked another annual meeting of the Biodiversity Information Standards network, otherwise known as TDWG, with the Norwegian capital Oslo hosting the event between 20 and 25 September.
\n\n\n\nTDWG is the international, non-profit organisation formerly known as the Taxonomic Databases Working Group, which develops and maintains the standards, such as Darwin Core, that allow biodiversity data to be shared, integrated and understood across institutions worldwide.
\n\n\n\nThe six-day programme was a meeting point for over 300 stakeholders from research, practitioner and corporate circles, with proceedings centred around one central topic – \"Research and Robot- Ready Biodiversity Data Standards\". In that sense, the themes of accessibility, responsibility, innovation and collaboration were foremost on the minds of participants as they contemplated how information on the biosphere should be extracted, consolidated and shared in the years to come.
\n\n\n\nAs a long-standing partner of TDWG, Pensoft Publishers served as both a sponsor and an exhibitor for the event. In the latter capacity, the company contributed with a joint booth for three of the EU-funded projects it is leading the outreach efforts for:
\n\n\n\nA number of other projects from Pensoft's communication portfolio also found a place in the display, including BEAGLE, OneSTOP, B-Cubed, MAMBO, OBSGESSION and BioAgora. Furthermore, 14 of the company's journals were in the spotlight, including Biodiversity Information Science and Standards (BISS), the official journal of the TDWG community (where all of the conference's abstracts are set to be published).
\n\n\n\nCrucially, the event also saw the official launch of ORPHO, Pensoft's new collaborative workspace for scientific writing. Founder and CEO Prof. Lyubomir Penev and CTO Teodor Georgiev conducted live demos of the tool, showcasing how manuscripts crafted within it with full version control for authors become discoverable, interoperable and FAIR-compliant.
\n\n\n\nIn the context of TDWG2026's agenda, they also presented ORPHO's integrated semantic annotation and communication instruments as part of the workshop \"How Literature Services can Support & Benefit from Biodiversity Publication and Data Standards?\" on 24 September. The workspace was received with anticipation and enthusiasm by attendees, with many expressing a desire to test it out for themselves.
\n\n\n\nThey were also treated with a tease of Pensoft's next large-scale endeavour, as Senior Communications Officer Peter Bozakov pulled the curtain behind the forthcoming communication platform for research projects Scientiana.
\n\n\n\n\n\n\n\nAs for other notable contributions to the wider conference agenda by researchers affiliated with Pensoft's publishing or project activities:
\n\n\n\nNotably, the Biodiversity Information Science and Standards (BISS) journal is the home for TDWG conference abstracts. TDWG 2026 presenters can currently submit their abstracts there and publish them in an extended abstract or mini-paper format completely free of charge. The journal also hosts abstracts from conferences held over the past decade. You can browse the TDWG 2026 collection here.
\n\n\n\nLast but not least, the company's sponsorship supported two awards at TDWG2026 (with a free publication in a Pensoft journal as an accompanying prize):
\n\n\n\nOverall, TDWG2026 proved itself a crucial forum for mapping out the future of biodiversity monitoring and the management of the myriad data streams that emerge from it. Pensoft is proud to be an important actor in this process, contributing with innovative tools and solutions at the heart of the interface between research and practice. As summarised by Prof. Penev:
\n\n\n\n\n\n\n\n\n\"Given its status as a large-scale conference centred around the future of biodiversity sciences in the age of AI, TDWG2026 was a crucial venue for unveiling Pensoft's forward-looking vision for academic publishing and science communication. The \"pearl\" of these pursuits is undoubtedly the ORPHO workspace, a tool that is well-placed to introduce a new standard when it comes to collaborative AI-FAIR research in Europe and beyond.\"
\n
***
\n\n\n\nFollow us on Bluesky, Facebook, X, and LinkedIn for future conference updates.
\n\n","doi":"https://doi.org/10.59350/zhfwk-5vr68","guid":"https://blog.pensoft.net/?p=20987","image":"https://blog.pensoft.net/wp-content/uploads/2026/10/SciComm-PR-2026-Instagram-2026-09-28T132928.181.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"w2baa-avn79","summary":"Last week marked another annual meeting of the Biodiversity Information Standards network, otherwise known as TDWG, with the Norwegian capital Oslo hosting the event between 20 and 25 September.","tags":["Advances In Pollinator Research","Agricultural And Environmental Modelling","Biodiversity Data Journal","BISS Journal","Individual-based Ecology"],"title":"Pensoft at TDWG2026: Pushing the boundaries of innovation in biodiversity data management forward","updated_at":1790861710,"url":"https://blog.pensoft.net/2026/10/01/pensoft-at-tdwg2026-pushing-the-boundaries-of-innovation-in-biodiversity-data-management-forward/","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Parusel","given":"Bernd"}],"blog":{"authors":null,"community_id":"ad6b31bb-31f3-4277-bcf1-7bc662d83893","created":1740441600,"current_feed_url":null,"description":"Netzwerk Fluchtforschung ist ein multi-disziplin\u00e4res Netzwerk von Wissenschaftler*innen die zu jeglichen Aspekten von Flucht und Fl\u00fcchtlingsschutz forschen.","doi":"https://doi.org/10.59350/fluchtforschung","favicon":"https://rogue-scholar.org/api/communities/ad6b31bb-31f3-4277-bcf1-7bc662d83893/logo","feed_format":"application/atom+xml","feed_url":"https://fluchtforschung.net/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://fluchtforschung.net/","issn":null,"language":"deu","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"fluchtforschung","status":"active","subfield":"3312","title":"Netzwerk Fluchtforschung","updated":1790859042,"use_api":true},"blog_name":"Netzwerk Fluchtforschung","blog_slug":"fluchtforschung","content_html":"It has been called the \"missing piece\" of the Common European Asylum System: a new EU regulation on the \"return of third-country nationals staying illegally in the Union\" was adopted by the Council of the European Union on 1 October 2026. The European Parliament had agreed on it in June. The regulation replaces the existing Return Directive and represents a radical shift to a system that prioritises forced return over \"voluntary\" departure. It also limits the rights of rejected asylum applicants and other people who do not, or no longer, hold a visa or residence permit. This blog posts examines the new approach, including the controversial concept of \"return hubs\". It argues that the return regulation is a clear expression of a European Union now fully embracing the migration narratives of the far-right.
\n\n
When the European Parliament greenlit the EU's new return regulation in June 2026, some MEPs gave standing ovations and chanted, \"send them back!\" in the Strasbourg hemicycle. While footage of these scenes caused some outrage, the far-right MEPs that celebrated probably knew quite well what they had achieved: They had succeeded in convincing a majority of MEPs that the EU needed a much harsher framework to make more people leave when their asylum claims have been rejected or their visas or residence permits have expired. The new rules, which will be analysed below, are based on limited rights and safeguards for such individuals, more enforcement powers for authorities, and ways for EU Member States to circumvent the problem that some third countries (i.e., non-EU countries) are reluctant to take their nationals back. Once the regulation is published in the Official Journal of the EU, it will become applicable one year later. Some components, however, including the legal groundwork for \"return hubs\" in third countries, will apply immediately.
\n\n
For years, maybe decades, politicians in the EU have complained that the return of rejected asylum seekers does not work well enough, arguing that asylum and migration systems are meaningless if those who are not granted a right to stay manage to remain in the EU anyway. The European Commission stated in 2020 that the low return rate \"erodes citizens' trust in the whole system of asylum and migration management\" and acts as an \"incentive for irregular migration\". Therefore, according to the European Commission, loopholes needed to be closed, and procedures streamlined, \"so that asylum and return work as part of a single system\".
\nWhile return rates, i.e., the percentage of people who actually leave once they are ordered to do so, are difficult to calculate and the relevant statistical data from Eurostat includes many caveats, it seems true that return decisions not always lead to returns. The most recent data from Eurostat shows that, in the first quarter of 2026, roughly 108,475 third-country nationals (i.e. non-EU nationals) were ordered to leave the territory of an EU country. During the same period, 34,550 third-country nationals, who had a return order, were returned to a non-EU country. Based on these two sets of data combined, \"return rates\" often circulate in the media and in public debates. For the first quarter of 2026, the return rate calculated in this way would be 32 percent. However, the two data sets do not connect. A person can, for example, be ordered to leave in one year, and the return is carried out the next year or later. Calculating return rates therefore only makes sense, and only as a very rough indicator, if data covering longer periods, at least several years, are used. Moreover, some people might leave without notifying the authorities, and some might receive a return order but later be given a right to stay.
\nBut even if we assume that return rates indeed are low, there are many reasons behind this (as explained here or here), such as violence and conflict in countries of origin, rejected asylum seekers having no travel documents or trying to abscond because they perceive their rejection as unjust and consider themselves at risk if they had to return, countries of origin refusing to readmit their nationals, disrupted travel connections, insufficient administrative or operational capacity in the Member States, and other practical, legal and political obstacles.
\nAn appropriate policy response to this problem would therefore need to be both holistic and nuanced. As suggested by ECRE, such policy would need to make sure that return orders are only issued when returns are indeed possible and realistic, make sufficient resources available to immigration and return authorities, and provide incentives to countries of origin to take people back and facilitate their reintegration. Data analysis suggests that countries with small return caseloads often have higher return rates than countries with high numbers of people to be returned. But instead of trying to understand the problems behind low return rates and attempting to find flexible solutions that also provide for regularisations or other alternatives to return, the EU's new approach is entirely based on the idea that progress lies in weakening human rights standards and exposing desperate people to more draconian rules.
\n\n
The new regulation was initiated by the European Commission in March 2025, after intense pressure from the EU Member States. The Council adopted a \"general approach\" on the draft regulation in December 2025, and the European Parliament finalised its position on 26 March 2026. After relatively quick negotiations between the institutions, the Parliament endorsed a compromise deal on 17 June 2026 and the Council adopted it on 1 October 2026, with only Spain voting against the proposal (and an abstention from Belgium).
\nOne of the main \"innovative solutions\" in the new regulation, and one that looks particularly radical, is that it establishes the legal basis for so-called \"return hubs\" in third countries. The purpose (as discussed here and here) is to enable Member States to deport people, who cannot be brought back to their countries of origin, to other countries outside the EU, provided one or several Member States manage to secure an \"agreement or arrangement\" with them. This was already included in the Commission proposal, but the Council and the Parliament widened the potential use of the concept by allowing not only adults, but also families with children to be sent to such hubs. Meanwhile, it is not (yet) clear how these \"return hubs\" would work in practice, where they would be created, how many people could be taken there, and what the EU side would have to offer to secure an agreement or arrangement with a target country. Whether or not people would be detained there, and if so for how long, is also unclear and will depend on the terms of each deal being made with a third country. Rwanda and Uzbekistan have been mentioned as possible candidates, but there are also speculations about Ghana, Senegal, Tunisia, Libya, Mauritania, Egypt, Uganda, Armenia, Montenegro and Ethiopia. As Dana Schmalz has argued, speaking of \"returns\" in this context is misleading; taking people to countries they have no connection with, and against their will, looks more like state-organised abduction.
\n\n
Another key feature of the regulation is that it limits the possibilities for third country nationals to challenge return decisions and find alternatives to returning. The overall idea seems to be that once a person is ordered to leave, they should have no other option than obeying or being removed. According to the new rules, a return decision can indicate one or several countries for a person to return to (for example, the person's country of origin, a third country where the person used to live before coming to the EU, a country of transit, or another country where the person has a right to enter and reside), or no such country. This can make it difficult for individuals to appeal a return decision with reference to the situation in a specific country. Return orders can even be issued when there is a risk of refoulement, i.e., the person would face persecution, torture, or other irreparable harm when returned. In such cases, which can occur \u2013 for example \u2013 if a person has not applied for asylum or where circumstances change after an asylum procedure, removals are to be postponed, but the return decision itself can remain. As appeals no longer have automatic suspensive effect, there is a risk that return decisions are executed before they can be remedied, which can violate the non-refoulement principle.
\nWhile the Commission's text had provided that a Member State may, at any moment in the return process, issue a residence permit for humanitarian or other reasons \u2013 thus regularising a person's status \u2013 this provision is missing in the final text. This can make it difficult (if not impossible) for asylum seekers, who are not found to be refugees or in need of subsidiary protection (or a national protection status), and who are therefore issued a return decision, to get legal residence on other grounds, such as family ties, humanitarian reasons, or poor health. The regulation could also interfere with national regularisation programmes, which raises concerns about unnecessary and disproportional EU overreach.
\nEscaping to another Member State is also something the regulation aims to rule out. A return decision issued by one Member State can be recognised and enforced by other Member States as well \u2013 these Member States are no longer required to make their own assessments or issue their own return decisions. While the Commission wanted to make mutual recognition mandatory, the Council was hesitant, most likely because it could put greater burdens on some Member States than on others. But the final version of the regulation does provide for the introduction of mandatory mutual recognition at a later point. Mutual recognition is to be facilitated with a new \"European Return Order\", a form containing the main elements of a return decision, which is shared via the Schengen Information System.
\n\n
Under the Return Directive of 2008, voluntary return was the preferred type of return. With the new regulation, this principle is lost. As return decisions do not need to include periods for voluntary departure anymore, forced return now becomes the standard scenario.
\nThe regulation also comes with expanded and harsher coercive measures, not least concerning detention. While under current rules detention may be imposed for up to 18 months, the Commission proposed a maximum period of 24 months. Even if it is not clear why a person that cannot be returned within 18 or 24 months might suddenly become removable once more time is added, the Council and the European Parliament added additional periods of up to 6 months in certain situations, and if a person moves to another Member State, a new period of detention can start. The catalogue of grounds for detention is widened as well, and Member States may add additional ones via national law. Overall, detention is transformed from a measure of last resort to a standard tool.
\nThe regulation also expands third country nationals' obligations to cooperate with the authorities, and the Member States get to undertake far-reaching \"investigative measures\" to prepare for or ensure removal. For example, they may search third-country nationals and their places of residence or \"other relevant premises\", seize personal belongings, and impose other investigative measures, where provided for in national law. This has reminded critics of the far-reaching powers of the police and customs authority ICE (Immigration and Customs Enforcement) in the USA and opens the door to new levels of violent immigration law enforcement.
\n\n
Overall, the EU's new return regulation creates a rigid system that weakens human rights standards as regards, for example, the non-refoulement principle, children's rights or the prohibition of arbitrary detention, raising doubts about the compatibility of the new rules with international and European human rights norms as well as the principle of proportionality, which says that actions taken by the EU must be necessary and suitable to achieve the desired end and not impose a burden on the individual that is excessive in relation to the objective sought to be achieved.
\nWhether the regulation will achieve its aim \u2013 to facilitate, increase and speed up returns \u2013 is highly uncertain. The regulation not only expands obligations for third-country nationals in the return procedure \u2013 it certainly also demands a lot from Member States' authorities, for example when it comes to the follow-up and enforcement of return decisions, detention capacity, and deportations. In situations where the number of people subject to return decisions is low, the new system might be manageable, but whenever caseloads are high, capacity problems can arise and costs can explode. Recent discussions about resuming returns to Syria and Afghanistan, the EU's new rules on safe countries of origin and safe third countries, and the fact that the return regulation limits alternatives to return suggest that return caseloads might indeed increase. Unless large numbers of people can be offloaded to \"return hubs\", which looks highly unlikely, there is a substantial risk that the number of individuals who get stuck in the new return system will grow, with no humane way out.
\nThe political circumstances surrounding the adoption of the new system are noteworthy as well. Historically, the European Parliament often attempted to \"soften\" hardline approaches of the Commission and especially the Council. Now, the two institutions worked side by side to make the Commission proposal even more draconian. This shows that EU policymaking on migration and asylum is currently in the hands of radical and far-right parties, and that large parts of the political centre and centre-right have normalised their ideas.
","doi":"https://doi.org/10.59350/05xf5-2ws85","guid":"https://fluchtforschung.net/?p=15971","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"vfdk9-smx71","summary":" It has been called the \"missing piece\" of the Common European Asylum System: a new EU regulation on the \"return of third-country nationals staying illegally in the Union\" was adopted by the Council of the European Union on 1 October 2026. The European Parliament had agreed on it in June. ","tags":["Englisch","Forschung","Common European Asylum System (CEAS)","Detention","EU"],"title":"Deportation at all costs? The EU's new regulation on returns","updated_at":1790859307,"url":"https://fluchtforschung.net/deportation-at-all-costs-the-eus-new-regulation-on-returns/","version":"v1"},{"authors":[{"contributor_roles":[],"name":"Atarraya"}],"blog":{"authors":[{"name":"Atarraya"}],"community_id":"f17066f5-0dbf-48d0-a413-b22a79861a94","created":1723852800,"current_feed_url":null,"description":"Nuestras historias","doi":"https://doi.org/10.59350/atarraya","favicon":"https://rogue-scholar.org/api/communities/f17066f5-0dbf-48d0-a413-b22a79861a94/logo","feed_format":"application/atom+xml","feed_url":"https://blogatarraya.com/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blogatarraya.com","issn":null,"language":"spa","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"atarraya","status":"active","subfield":"1202","title":"BLOG ATARRAYA","updated":1790331332,"use_api":true},"blog_name":"BLOG ATARRAYA","blog_slug":"atarraya","content_html":"DR \u00a9 Karina Valeria Dicker
\n\n\n\nTextil: t\u00e9cnica textil, telas te\u00f1idas, en forma artesanal, con tintes naturales elaborados por la artista.
\n\n\n\nYerba Buena, Tucum\u00e1n, Argentina, 2025.
\n\n\n\nEsta es una reproducci\u00f3n digital, con fines de divulgaci\u00f3n, de una obra original, todos los derechos de autor y reproducci\u00f3n est\u00e1n reservados por el artista.\u00a0
\n\n\n\nRedes de la artista
\n\n\n\n\n\n\n\nRedes de Atarraya
\n\n\n\n","doi":"https://doi.org/10.59350/4c4h9-s6j95","guid":"https://blogatarraya.com/?p=8071","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"46bk3-jtg76","summary":"DR \u00a9 Karina Valeria Dicker Textil: t\u00e9cnica textil, telas te\u00f1idas, en forma artesanal, con tintes naturales elaborados por la artista. Yerba Buena, Tucum\u00e1n, Argentina, 2025. Esta es una reproducci\u00f3n digital, con fines de divulgaci\u00f3n, de una obra original, todos los derechos de autor y reproducci\u00f3n est\u00e1n reservados por el artista.","tags":["Artes Visuales","Sin Categor\u00eda","N\u00famero 32"],"title":"Cartograf\u00eda de un Paisaje Modificado","updated_at":1790852707,"url":"https://blogatarraya.com/2026/10/01/cartografia-de-un-paisaje-modificado/","version":"v1"},{"authors":[{"affiliation":[{"id":"https://ror.org/00dyx6f45","name":"Thoth Open Metadata"}],"contributor_roles":[],"family":"Steiner","given":"Tobias","url":"https://orcid.org/0000-0002-3158-3136"}],"blog":{"authors":null,"community_id":"59fc3cc5-6c51-47b4-b195-22ac5cc89d24","created":1672617600,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/flavoursofopen","favicon":"https://rogue-scholar.org/api/communities/59fc3cc5-6c51-47b4-b195-22ac5cc89d24/logo","feed_format":"application/atom+xml","feed_url":"https://flavoursofopen.science/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://flavoursofopen.science","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"flavoursofopen","status":"active","subfield":"1710","title":"Flavours of Open","updated":1790798459,"use_api":true},"blog_name":"Flavours of Open","blog_slug":"flavoursofopen","content_html":"Toby Steiner\u00a0 & Vincent W.J. van Gerven Oei
\nPreprint; to appear in\u00a0tendencia editorial UR \u2013 N\u00ba 41 Especial \u00b7 Universidad del Rosario, Bogot\u00e1, 2026\n
Across scholarly communications, artificial intelligence is being touted as both a diagnosis of and a miracle cure for the wide variety of issues plaguing the sector. The diagnosis is familiar: too much information, too little time; too many records, too few cataloguers; too many systems, too little interoperability. The proposed cure is automation at scale\u2014automation facilitated through AI.
\nThe massive increase in higher education institutions' (HEI) spending on the technology (UNESCO, 2025) points to a rather unique persuasiveness of this framing by commercial vendors over the last few years, particularly among upper management executives without direct access to technical expertise: to them, AI is being sold as the one \"magic bullet\" to fix the many underlying problems they had already been persuaded to invest in over the previous years. Here, the pivot to AI can be understood as just the next step in a larger move to fully outsource HEI infrastructure to commercial vendors (see, e.g., Bowie, 2025).
\nMeanwhile, the underlying plethora of problems becomes even more severe: metadata work remains under-resourced. Small and scholar-led publishers commonly lack the technical capacity of large commercial firms. Library and publishing workflows are fragmented across systems, standards, and platforms. Books and chapters remain particularly difficult to describe, distribute, discover, and preserve consistently.
\nYet the promised all-purpose solution of AI conceals a more consequential set of questions. What kind of automation is being normalised? Who owns and governs the systems that enable automation? What costs\u2014financial, environmental, and social\u2014disappear behind the shiny interface of an AI agent? Whose actual labour is displaced, subordinated, or rendered invisible? Who looks after and is ultimately responsible for code churned out as AI slop (Weedon et al., 2026), which some have declared our societies' modern asbestos, being shoveled into the walls of our collective digital ecosystems (Doctorow, 2025)? What happens when public and community-produced knowledge becomes an input to proprietary systems that cannot be inspected or governed by the communities from which that knowledge originated?
\nFor Thoth Open Metadata, the non-profit we work for, these are not abstract concerns or mere philosophical ponderings. The underlying issues, one might argue, are very much about the overall purpose, ownership, and future of scholarly infrastructure.
\nTo provide a bit of context on what we do: Thoth is a non-profit, community-led open platform for managing and disseminating fully open, reusable, high-quality metadata for books and chapters. It was developed by and for publishers, with a particular focus on open-access titles and an overarching aim to support small, scholar-led, university, and library presses in tackling a variety of structural barriers to participation in the book supply chain. Thoth's legal form, a UK Community Interest Company limited by guarantee, reflects our explicit commitment to community benefit rather than shareholder value. Thoth's technical architecture is open source; its Application Programming Interfaces (APIs) are open; and its metadata is released into the public domain (via a CC0 dedication made explicit within the metadata records) to facilitate open reuse across libraries and other stakeholders active in the book supply chain.
\nThis positionality also shapes Thoth's approach to AI. For us, the issue is not whether automation can assist with metadata work. It already does. The more important question is whether it can strengthen communities and professional agency or reorganise them around the profit-making priorities of extractive proprietary systems. We are strongly opposed to reducing the future of metadata and cataloguing to yet another layer in an extractive corporate AI stack. Instead, we argue, AI tools should form part of a broader public knowledge infrastructure that is transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it.
\nHence, for us, imagining an alternative approach to the current commercial AI regime would be guided by the following set of rules:
\nIn the following sections, we will expand on each of these points by discussing four major issues that can be identified under the current regime of commercial AI provision. Following this discussion, we will outline how the work we do within the broader publishing community might provide an alternative, bibliodiverse and equitable model that serves the communities it is governed by, rather than stakeholders' pockets.
\nRecent commercial AI is organised around an expansive and consolidating model of scale. Larger models require larger datasets, greater computational capacity, more data centre infrastructure, and deeper concentrations of capital. Scale here functions simultaneously as a technical strategy and a competitive weapon. In this environment, scale does not simply mean serving more users. It often means consolidating control over the infrastructures through which people work, publish, search, and communicate. Once functions are bundled together, institutions become progressively dependent on a small number of providers.
\nThe concentration of AI provision therefore can well be understood as the logical continuation of a longer process of platformisation (Poell et al., 2019) in scholarly communication. The already-familiar issues of vendor lock-in, opaque analytics, restricted metadata reuse, and extractive work linked to maintaining metadata quality in the context of closed walled gardens have not disappeared. On the contrary, AI is very likely intensifying them by adding another proprietary front-house interface and smoke screen between users and these complex issues (see also Van Gerven Oei 2026).
\nA practice tied to the neoliberal notion of \"scaling up,\" which we see rampantly at play with the major commercial AI providers, is the provision of services that can be sustained even while remaining heavily indebted, environmentally damaging, socially extractive, or politically vulnerable. A commercial infrastructure may appear stable because it has substantial financial backing propped up by circular funding (Down & Milmo, 2026), but it can still expose communities to acquisition, strategic redirection, service withdrawal, sudden price changes, or revised data policies.
\nFor open infrastructure, sustainability must include governance, technical openness, succession, community accountability, and the ability of users to continue their work if an organisation changes or disappears.
\nThis has direct implications for the use of AI. Uptake of a heavily cross-subsidised AI service may appear inexpensive today, but it will also create deep, long-term dependencies. Its prices, environmental practices, access terms, or data policies may change once institutions have reorganised their workflows around new profitability targets. Commercial AI provision also externalises costs. Users absorb the labour of checking and correcting outputs; open infrastructures absorb the bandwidth and server costs generated by automated harvesting through bots and agents; communities whose work informs models may receive neither recognition nor material support.
\nTied to the above, the overall rhetoric around AI encourages users to experience it as frictionless. A prompt is entered; an answer appears. The underlying infrastructure becomes opaque, yet AI remains a materially resource-intensive technology. Its provision depends on massive data centres, global semiconductor supply chains, public electricity grids, environmentally damaging cooling systems, increasingly scarce water resources, physical land, and continuous hardware replacement. As recent research by Aczel et al. (2026) argues, AI's environmental impact must be assessed across carbon, water, and land footprints, rather than through electricity use alone. The report emphasises that inference, i.e., the routine use of deployed systems, can become a major source of resource consumption at scale.
\nInvestigations into data centre expansion have also drawn attention to facilities operating or under development in water-stressed regions (e.g., Barratt et al., 2025). This complicates the notion that AI necessarily produces efficiency. A process may appear efficient to the user because environmental burdens have been displaced elsewhere. The same applies to labour. The time required to verify, correct, and contextualise AI output is often omitted from productivity claims. So too is the cost of maintaining defensive systems against aggressive automated scraping. A serious assessment of AI therefore needs to include the infrastructural footprint of its entire stack, from geology to the user interface (see Bratton, 2016). Universities, libraries, publishers, and funders should demand environmental transparency and determine whether a system's computational intensity is proportionate to the task.
\nOne might argue that, particularly in the field of metadata and cataloguing, the problem space at hand does not require a general-purpose large-scale language model. As we show below, the issues can be addressed more reliably through structured data, validation rules, authority files, controlled vocabularies, and narrowly targeted, purposeful automation.
\nThe consequences of commercial AI's extractive behaviour are already visible at the infrastructural level. In the books space, aggregators such as OAPEN and the Directory of Open Access Books have reported aggressive bot traffic that has affected platform stability and required traffic-management and bot-protection systems (Davison et al., 2026).
\nThe Public Knowledge Project faces a similar structural challenge across the distributed ecosystem of Open Journal Systems and Open Monograph Press installations. Because these systems are often hosted by universities, libraries, societies, and small publishers, the costs of automated scraping are distributed across institutions with very different technical capacities.
\nProject Gutenberg provides another example. Its public-domain corpus is openly available and widely useful, but the infrastructure that serves it is not cost-free. Repeated large-scale crawling consumes bandwidth and processing capacity and relies on the labour of volunteers who digitise, proofread, structure, and maintain the collection.
\nWikimedia faces the same conflict on an even larger scale. Open knowledge infrastructures pay specialists to curate, store, serve, and protect information, while automated systems create additional demand and may simultaneously redirect users away from the original source. This creates both operational and wider-ranging social imbalances. Operationally, community infrastructures bear the costs of access. Socially, they lose the recognition, participation, and support that accompany direct engagement.
\nAs Van Gerven Oei and Fradenburg Joy (2025) point out, the problem is not automated access per se. Preservation, indexing, and metadata exchange have always relied on machine-to-machine communication. The problem is uncoordinated extraction without restraint, attribution, provenance, or reciprocity. Responsible (re)users should prefer APIs, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) endpoints, bulk datasets, mirrors, and rate-limited feeds where available. They should properly identify themselves, avoid repeated retrieval of unchanged resources, and contribute materially when their use creates high costs, in line with fair-use principles. Open access cannot mean that public-interest infrastructures are expected to subsidise limitless private accumulation.
\nCurrent commercial AI provision challenges basic assumptions about openness. The promise of open access was to enable knowledge to be read, shared, reused, preserved, and built upon. Yet open availability is increasingly interpreted by commercial AI companies as permission for unrestricted extraction.
\nThe controversy surrounding Taylor & Francis's agreement with Microsoft clearly illustrates the underlying problem. Authors reported that they had not been meaningfully consulted about the use of their work for AI-related purposes (Battersby, 2024). Recent investigations into the use of pirated book collections for model training reveal even more serious failures in consent and accountability (Reisner, 2025). These developments point to the limitations inherent in current implementations of an open knowledge paradigm\u2014openness that focuses solely on a legal definition can be easily co-opted and exploited by actors whose practices are neither reciprocal nor transparent.
\nThe Wikimedia Foundation's recent discussion of the \"social contract of free knowledge\" offers a valuable framework for understanding these tensions (Rogers, 2026). Free knowledge depends on more than mere availability; it relies on relationships among contributors, publishers, infrastructures, and (re)users. Attribution, provenance, and the possibility of contributing back to the wider ecosystem are part of the social arrangement that sustains a commons. Commercial AI systems can disrupt this arrangement in several ways. They ingest material without meaningful disclosure, or any disclosure at all. They provide answers without adequate citation. Their system design may keep users inside proprietary interfaces, preventing them from encountering original sources or the communities that maintain them. They may return little to the commons from which they derive value. All of this points not simply to a failure of etiquette; it signifies an illegal transfer of control and power over content away from its originators.
\nWhen users receive information through one of the large commercial AI interfaces\u2014be they Gemini, Claude, ChatGPT, or any other recent \"silver bullet\" offerings available, often integrated wholesale into browser interfaces\u2014without seeing its sources, the provider effectively co-opts the relationship between reader and knowledge producer. The originating author, contributor, publisher, repository, catalogue, or community is rendered invisible (for more on this, see, e.g., Strauss et al., 2025). The commercial AI system appears to be the source, and all the labour involved in creating the information is devalued and rendered void. For us, this is a clear violation of the spirit inherent in the participatory commons that open knowledge represents.
\nRelated to the above, the question of provenance becomes a central issue. In web search, proper attribution is often treated as a secondary concern, added after an answer is generated. In scholarly communication, however, it is a crucial element ingrained in good academic practice. Citation allows readers to evaluate evidence, trace claims, understand context, and identify the people and organisations responsible for a work. It creates pathways between users and knowledge communities.
\nThe same principle applies to metadata. A bibliographic record may combine information from title pages, publisher feeds, authority files, DOI registrations, institutional identifiers, catalogues, and controlled vocabularies, among many other sources. If an AI system synthesises these sources without explaining which assertions came from where, errors become difficult to diagnose and accountability becomes diffuse.
\nOne might argue that a meaningful commitment to tracking provenance should therefore extend beyond providing a generic list of links. For example, it might be advisable for systems to distinguish publisher-supplied metadata from inferred data or from input generated by aggregators and libraries once metadata have been released into the wider supply chain. Ideally, metadata could indicate when records have been mapped, transformed, or enriched, and could also document the vocabularies and authority sources used.
\nThird-party providers should also disclose which catalogues, repositories, websites, and datasets have contributed to their systems, when the material was collected, and how it was processed. As we at Thoth have repeatedly argued, in line with open data experts (see, e.g., ICOLC, 2023; Courtney et al., 2024), any metadata record should be placed under a public-domain dedication (e.g., CC0) to ensure easy, frictionless downstream reuse, with the relevant licensing statement explicitly included in the metadata record.
\nWithout such measures, commercial actors, and through them, AI, can not only reuse open knowledge but also erase the chain of provenance that makes that knowledge trustworthy.
\nThe use of generative AI can be particularly problematic when plausibility is not sufficient. Metadata and cataloguing depend on verifiable outcomes. A record should accurately identify a specific work, edition, contributor, licence, subject, or persistent identifier. It must be consistent enough to move across systems and precise enough to support discovery, dissemination, and preservation.
\nGenerative AI systems, by contrast, are designed to produce plausible language. That design difference creates a fundamental mismatch with metadata work\u2014a mismatch that has been identified at both functional and ethical levels (Frenzel, 2025). A recent study of ChatGPT even classifies the usability of models such as ChatGPT as \"bullshit\" (Hicks et al., 2026).
\nThe underlying problem is not merely that large language models (LLMs) sometimes make mistakes; it is that, with AI, producing an authoritative-sounding answer does not equate to generating a verifiable output. In cataloguing, this limitation is consequential. Fabricated ISBNs, licences, contributor identities, or subject terms can quickly propagate into catalogues, aggregator databases, repositories, and knowledge graphs. As records move further from their source, errors become increasingly difficult to identify and correct. One might argue that, in the context of cataloguing and metadata work, having no metadata at all would be preferable to having wrong metadata.
\nResearch on AI-assisted cataloguing has thus called for extreme caution rather than a quick replacement of existing metadata and cataloguing workflows. Experiments at the U.S. Library of Congress found greater potential for AI in relatively straightforward descriptive fields than in areas such as subject and genre assignment, where substantial human review remained necessary (Saccucci & Potter, 2025). An earlier study of AI-assisted MARC21 record creation reached a similarly qualified conclusion (Taniguchi, 2024). In a similar vein, AI has been shown to produce fabricated titles, inaccurate identifiers, missing fields, malformed structures, and invented content (Aycock, 2025). As a consequence, the Program for Cooperative Cataloging (2025) stresses the need for human review to ensure provenance, accountability, and explicit responsibility for machine-generated metadata.
\nIt also seems important to highlight the need to honestly address and assess the verification labour that is becoming necessary to control and oversee output generated by AI. When every generated field must be checked against an authoritative source, AI-driven automation quickly redistributes labour rather than reducing it. Correcting a plausible but misleading record can require more labour and expertise than creating an accurate record directly.
\nBuilding on the issues above, it may be worth considering a number of central questions governing the use of AI: Who governs the system? What specific task does it perform? Can its outputs and assumptions be inspected? Can users leave the system? Does it strengthen or diminish professional agency? What environmental and social costs does it create?
\nThe concept of scaling small (Barnes & Gatti, 2019; Adema & Moore, 2021; Adema et al., 2024) that informs Thoth's activities within a wider network of open infrastructures may serve us here and offer a useful counter-positioning. The concept's core argument does not romanticise smallness or reject the need for coordination. It challenges the assumption that infrastructures must grow through concentration, standardisation, and market capture to be successful (see, e.g., Sutton, 1991).
\nIn contrast, scaling small entails a relational model of collaborative development. Capacity can grow through the federation of interoperable systems that communicate via open data and open APIs. A diverse plurality of organisations supports one another through the mutual sharing of resources and knowledge, all grounded in a shared set of values and participatory governance models. The overall objective is not to construct a single platform capable of absorbing every function and content type, but to cultivate an ecosystem in which specialist infrastructures co-operate while remaining accountable to their communities.
\nApplied to AI, this distinction is fundamental. A system that helps publishers translate structured metadata into all major industry formats (e.g., ONIX, KBART, MARC21, etc.) in an openly licensed way is not equivalent to a proprietary model that ingests vast amounts of scholarly content under questionable legal frameworks and then generates outputs of uncertain provenance and reliability. Both of these systems may involve automation, but they embody very different assumptions about ownership, control, accountability, and value. One expands distributed capacity. The other effectively concentrates and flattens it.
\nNon-profit, community-led open infrastructures such as Thoth Open Metadata and the Open Book Collective offer a practical articulation of this alternative. Its call to \"seed\" a not-for-profit, community-led ecosystem for open access books (Steiner et al., 2025) deliberately rejects the aspiration to build a single dominant platform. \"Seeding\" here suggests cultivation rather than capture: a landscape of organisations rooted in particular communities and areas of expertise, connected through common values, open standards, and shared infrastructures. Within this ecosystem, publishing, metadata, preservation, collective funding, discovery, and governance need not be controlled by a single organisation. They can be distributed across a network of community-led services that cooperate without merging. This is a non-competitive philosophy in a substantive sense. The success of one infrastructure does not require the disappearance or absorption of another. Organisations can exchange data, coordinate development, share documentation, and direct users toward one another. Their relationships need not be organised primarily through market conquest.
\nThe distinction matters because commercial providers frequently treat integration as a rationale for consolidation. Community-led infrastructures can instead treat interoperability as a condition for plurality. When systems communicate via open APIs, when data can be exported in open formats, and when records can move across systems without proprietary restrictions, cooperation becomes possible without institutional assimilation. Drawing on Ostrom (2009), Thoth's role in this ecosystem is deliberately \"bounded.\" It does not seek to become the one-and-only infrastructure for open access books. Its purpose is to provide an open, reusable translational metadata layer through which books and chapters can move among publishing systems, repositories, catalogues, aggregators, knowledge bases, preservation services, and discovery platforms. In this context, boundedness is a strength. It makes interdependence possible without making any one organisation indispensable.
\nCurrent discourse on the promises of greater automation through AI often collapses all automation into a single category, obscuring important differences among tools, purposes, and governance models. In some contexts, AI-augmented automation might actually prove useful. One example is the development of Annif, an open-source toolkit for automated subject indexing developed by the National Library of Finland that works with controlled vocabularies and selected training collections. Its scope is bounded, its outputs can be evaluated, and its limitations can be studied in relation to particular languages, vocabularies, and domains. The German National Library's work on automated subject cataloguing (Poley et al., 2025) offers another example of institutionally governed machine assistance.
\nSuch systems operate within professional contexts, are subject to explicit evaluation, and align with established bibliographic responsibilities. These tools differ substantially from general-purpose proprietary models presented as universal solutions. They are narrower, more transparent, and more accountable. Their usefulness does not depend on replacing professional human judgement. This distinction is also central to Thoth's position. The relevant choice is not between technological enthusiasm and refusal; it is between technologies organised around different institutional values and modes of operation. When AI is used as a tool overseen by a capable specialist and helps them automate tedious tasks, it might actually prove useful.
\nThoth's development work between 2023 and 2026 illustrates how automation can be organised around community needs rather than labour substitution (Steiner, Arias, Gatti, et al., 2026). A central objective has been to reduce duplication of work imposed on publishers by fragmented supply chains. Instead of repeatedly entering and reformatting the same information for different recipients, publishers can maintain rich metadata in a single open record and then generate outputs in multiple standards as needed, including ONIX, MARC, crossref XML, KBART, JSON, CSV, and BibTeX.
\nBy implementing the good metadata practice recommendations outlined in a recent report (Steiner, Arias, Bennett, et al., 2026), persistent identifiers such as ORCID and ROR can be incorporated into these records. Controlled vocabularies such as Thema and BISAC can be applied in structured form. Open APIs enable other services to retrieve and reuse the open data available through Thoth. Providing an OAI-PMH endpoint enables harvesting via an established repository protocol. Bulk-ingest workflows reduce repetitive entry and make it easier to bring existing records into the system. These forms of automation remove avoidable friction while leaving the publisher in control of the underlying metadata.
\nCommercial AI systems frequently treat linguistic variety as a problem to be overcome by using larger training corpora. To meet the need for ever more data to feed these training corpora, commercial AI companies have recently started to include the wholesale ingestion and subsequent destruction of pre-2022 printed books (see, e.g., Landymore, 2026). If metadata production, translation, discovery, and evaluation are mediated by the same commercial systems, diversity at the level of content risks becoming flattened into uniformity at the level of infrastructure.
\nCurrent modes of commercial AI provision in the area of metadata and discoverability intensify this risk. Systems trained predominantly on Western languages, publishing models, and disciplinary conventions are prone to reproducing those priorities, especially when marketed as \"universal.\" Materials that fall outside the patterns on which AI has been trained may be mistranslated, misclassified, and/or made less visible. Local categories and diverse contributor roles may be forced into schemas that do not adequately represent them.
\nFor books, these risks are especially acute. Books and chapters support long-form, multilingual, edited, experimental, and locally situated scholarship that is particularly relevant to the Humanities and Social Sciences. Their metadata may need to describe multiple contributor roles, multiple relational connections among, for example, a series, title, and its chapters, rights statements, accessibility features, translations, and locally meaningful subject classifications.
\nIt is worth noting that the complexities inherent in long-form publishing are not a defect to be flattened or removed. Quite to the contrary, they are part and parcel of the cultural and bibliographic diversity that defines academic research cultures. Scholarly infrastructure that cannot represent such complexity does more than simply produce subpar, incomplete records. It limits which publications become discoverable.
\nBy contrast, open metadata infrastructure approaches it as a design obligation. Languages, scripts, and regional conventions should be represented explicitly and structurally, not reconstructed probabilistically from the perspective of dominant linguistic patterns. Thoth's commitment to multilingual metadata and structured bibliographic relationships, informed by its commitment to bibliodiversity (Adema et al., 2024), reflects an effort to preserve underlying diversity at the infrastructural level. The intention is not to treat every publication as an instance of the most common model, but to develop systems sufficiently flexible to represent the beautiful variety that makes up the scholarly record across disciplines.
\nThoth's service model follows the same logic. Thoth Oasis provides free metadata management and open-format exports. Thoth Obelisk adds dissemination, DOI registration, hosting, and archiving; Thoth Sphinx provides privacy-conscious usage statistics; and Thoth Pyramid enables publishers to host websites and integrated catalogues under their own web domains. All of these services integrate and are interoperable, with a rich array of industry standards maintained by multiple communities (e.g., ONIX, MARC) and third-party services often maintained by non-profit organisations, such as Crossref, DataCite, ORCID, and ROR, as well as aggregators and repositories, such as the OAPEN Foundation, the Directory of Open Access Books (DOAB), the Internet Archive, Zenodo, JSTOR, Project MUSE, and the OPERAS Metrics service (Arias et al., 2025). In addition, data and content are submitted to commercial platforms to maximise visibility of books and chapters while still maintaining the open nature of the content and its metadata upstream.
\nThe underlying objective here is not to create yet another expanding proprietary stack. Publishers can use modular services while always retaining control over their identity, records, and workflows. We would argue this is automation without enclosure: the reduction of unnecessary repetition without transferring authority from communities to opaque algorithmic black boxes.
\nDiscussions about academic books at last year's Guadalajara Book Fair (Ramalho & Steiner, 2025) underscore why bibliodiversity, metadata autonomy, and interoperability need to be considered together. Participants in a metadata seminar at the Fair identified recurring problems in Ibero-American academic publishing: fragmented records, missing identifiers, incomplete rights information, inconsistent metadata, and weak interoperability among publishers, libraries, indexes, and evaluation systems. As a result, many academic books from Latin America remain poorly represented in discovery environments even when their content is openly accessible (see also the preliminary results of the CSIC-led Cartograf\u00eda de la edici\u00f3n acad\u00e9mica iberoamericana project).
\nWe do not want to propose a solution that assimilates diverse publishing communities into a single global platform. On the contrary, the focus ought to be on developing infrastructures that express local and linguistic specificity while enabling information exchange through shared standards. The respective approaches of SciELO Livros and Thoth illustrate this possibility. SciELO Livros has a long track record of public infrastructure development grounded in Latin American scholarly communication, including book and chapter records, ePub production, DOI management, preservation, and metadata exchange. Thoth addresses related challenges through open metadata management and dissemination.
\nHere, we see the value of community-led collaboration precisely in the fact that neither system needs to absorb the other. Through interoperable open protocols, infrastructures can connect while preserving regional, institutional, and epistemic autonomy. This is scaling small in practice: difference becomes the basis of cooperation rather than an obstacle to it.
\nOpen APIs are therefore not merely technical conveniences. They are governance instruments. They allow organisations to collaborate without consolidating ownership. In this context, open, reusable public-domain metadata serves a similar purpose and acts as an antidote to the extractive practices outlined above by enabling circulation, subsequent enrichment, and redistribution without tying records to a single proprietary service. Interoperability must, however, be based on trust. Systems need to exchange information in transparent, inspectable, and reversible ways. Participants should be able to identify where data originated, how it has been transformed over time, and under which conditions reuse is permitted. These conditions should then be enforced across the ecosystem so that participants can trust their applicability.
\nA commons survives because people can contribute to it. This is one of the strongest arguments in the Wikimedia report. AI interfaces often provide no meaningful route for users to correct errors, improve source materials, or support the communities from which the reproduced information originates. In short, much knowledge flows out of the commons; very little flows back. Generative systems can intensify this asymmetry by producing large quantities of plausible but unverified material. Creating a summary, subject description, or catalogue record may take seconds. Verifying it may require specialist expertise and substantial time. The cost of generation is borne by the model provider. The cost of correction is handed over to the communities without prior consultation or any protocol in place to facilitate those mediations.
\nFor metadata, this is particularly damaging. Shared records improve when publishers, libraries, and researchers collaborate to add identifiers, correct affiliations, refine subject classifications, and update rights information. Those improvements should circulate through accountable workflows rather than remain trapped within proprietary products and walled-garden ecosystems. Thoth's open APIs and publisher-controlled records are intended to support this form of exchange. Fully open metadata released under a CC0 dedication can move freely between systems, be enriched, and return to the wider ecosystem without any single company becoming the permanent owner and thus the controller of the process.
\nCory Doctorow's account of the recent AI bubble further sharpens the distinction between useful technical capabilities and the commercial ideology surrounding them (2026). Doctorow argues that AI is being marketed not merely as software but as a vehicle for replacing labour and capturing part of the resulting wage savings. In Doctorow's reading of automation theory, \"centaurs\" can be understood as information professionals who use AI as a productive tool to improve their work practices. His concept of the \"reverse centaur\" then describes a worker subordinated to cater to the needs of the machine: required to monitor automated output, to correct its failures, and absorb responsibility when AI is wrong (Ouellette, 2026).
\nThe \"reverse centaur\" metaphor seems a particularly pertinent warning for cataloguing and metadata work. A genuinely assistive system might identify candidate subjects, reconcile identifiers, or detect inconsistencies. A reverse-centaur workflow would increase production targets, reduce specialist staffing, and leave a substantially smaller number of professionals responsible for checking ever-increasing volumes of machine-generated records. The difference lies in who controls the workflow and for whose benefit it is organised.
\nAs a silver lining, Doctorow (2026) suggests that a number of useful tools might be salvaged from the eventual collapse of the current, highly speculative AI bubble. Indeed, open models, commodity hardware, and specialised systems\u2014such as Annif or other specialist workbenches that use AI, e.g., for automatically extracting references\u2014may prove useful once detached from claims of universal automation and staff replacement.
\nThis possibility aligns well with Thoth's positionality. The aim should be not to reject computational tools but to situate them within open infrastructures governed by community-led values. A useful system will be transparent, inspectable, proportionate, and subordinate to human and community purposes. It will strengthen professional agency rather than reduce people to monitors of machine output. It will operate on open standards rather than deepen proprietary dependence.
\nThe debate about AI is often framed as a choice between embracing and resisting technological progress. A more meaningful choice concerns the technological and institutional future that scholarly communities are prepared to build.
\nAs described above, one path leads to further concentration: a small number of commercial providers control models, interfaces, data, and access (Hao 2026). Knowledge is extracted from open sources, captured and processed by opaque systems, and delivered through proprietary environments. Professional labour is reorganised around machine output. Substantial environmental costs remain largely hidden. Communities become dependent on systems they cannot govern.
\nThe other path is more demanding because it requires network-building rather than adopting a single silver-bullet product. It involves creating open, interoperable, community-led infrastructures that support shared workflows without centralising authority. Thoth's contribution to this second path is necessarily partial. We provide open-source tools, open APIs, and openly reusable public-domain metadata for books and chapters. Our contribution seeks to strengthen publisher agency, reduce duplicate labour, and enable collaboration among infrastructures rather than replace them.
\nThis approach is grounded in trust\u2014but not blind trust. It is trust supported by transparency: inspectable code, visible provenance, open standards, good metadata practices, documented interfaces, accountable governance\u2014and the belief that publishers should retain control over their metadata and operational infrastructure.
\nIt is also grounded in a different understanding of efficiency. For us, efficiency should not mean shifting costs onto workers, communities, or the environment. It should mean reducing unnecessary duplication while preserving quality, agency, and participation that contribute to the enhancement of shared knowledge across infrastructures.
\nThe emerging, broader ecosystem envisioned through active collaborations among open infrastructures such as Thoth Open Metadata, SciELO Livros, the Public Knowledge Project, Open Book Collective, OAPEN, DOAB, Project Gutenberg, Wikimedia, Pressbooks, the many small- to medium-sized publishers, and an ever-increasing number of academic libraries points toward such a future.
\nThese infrastructures differ in purpose, scale, and history. Their value lies not in becoming uniform, but in strengthening bibliodiversity.
\nWe believe the future of metadata and cataloguing should not be reduced to yet another layer in an extractive corporate AI stack. It should form part of a broader public infrastructure for knowledge: transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it. For open-access books, for free knowledge, and for the infrastructures on which they depend, this is the more ambitious alternative\u2014we feel it is also the one worth fighting for.
\nThe bibliography of all sources referenced in this article are also available via an open Zotero collection:\u00a0https://www.zotero.org/groups/6626908/ai_and_metadata
\nAczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). The Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints. United Nations University Institute for Water, Environment and Health (UNU INWEH). https://doi.org/10.53328/INR26RMA002
\nAdema, J., & Moore, S. A. (2021). Scaling Small; Or How to Envision New Relationalities for Knowledge Production. Westminster Papers in Communication and Culture, 16(1), Article 1. https://doi.org/10.16997/wpcc.918
\nAdema, J., Barnes, L., Deville, J., Fathallah, J., Gatti, R., Grady, T., Hopkins, K., Hughes, A., McGann, C., Sanders, K., & Steiner, T. (2024, December 19). The COPIM Perspective on Bibliodiversity. COPIM. https://doi.org/10.21428/785a6451.86a892a7
\nArias, J., Van Gerven Oei, V. W. J., Gatti, R., Higman, R., Hillen, H., O'Connell, B, Ramalho, A., & Steiner, T. (2025, October 20). Open Access, Open Data, Open Archiving: Liberating Metadata Flows across the OA Books Landscape. Zenodo. https://doi.org/10.5281/zenodo.17340814
\nAycock, M. (2025). Prompting Generative AI to Catalog: The Promise and the Reality. College & Research Library News, 86(09). https://doi.org/10.5860/crln.86.10.423
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\nBattersby, M. (2024, July 19). Academic Authors \"shocked\" After Taylor & Francis Sells Access to Their Research to Microsoft AI. The Bookseller. https://www.thebookseller.com/news/academic-authors-shocked-after-taylor–francis-sells-access-to-their-research-to-microsoft-ai
\nBowie, S. (2025). Small Changes: Taking Back Control of Research through Open Software. Edinburgh Open Research, 4: Conference Proceedings. https://doi.org/10.2218/eor.2025.10953
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\nDavison, S., Wa\u0142ek, A., & Varachkina, H. (June 16, 2026). Traffic Management and Bot Protection for the OAPEN Library and DOAB: Implementing Cloudflare and Anubis. OAPEN: A World of Scholarly Books: Open to All, Built to Last. https://doi.org/10.58079/16es1
\nDoctorow, C. (2025, September 27). Pluralistic: The Real (Economic) AI Apocalypse Is Nigh (27 Sep 2025). Pluralistic: Daily Links from Cory Doctorow. https://pluralistic.net/2025/09/27/econopocalypse/
\nDoctorow, C. (2026, January 18). AI Companies Will Fail. We Can Salvage Something From the Wreckage. The Guardian. https://www.theguardian.com/us-news/ng-interactive/2026/jan/18/tech-ai-bubble-burst-reverse-centaur
\nDown, A., & Milmo, D. (2026, February 5). What Does the Disappearance of a $100bn Deal Mean for the AI Economy? The Guardian. https://www.theguardian.com/technology/2026/feb/05/disapperance-100bn-deal-ai-circular-economy-funding-nvidia-openai
\nFrenzel, F. (2025). Smart Enough to Mislead: The Functional Shortcomings and Ethical Dilemmas of Generative AI Use in Metadata Work. Catalogue and Index, 211, 39\u201357.
\nHao, Karen. 2026. Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI. Penguin Books.
\nHicks, M. T., Humphries, J., & Joe Slater, J. (2024). ChatGPT Is Bullshit. Ethics and Information Technology, 26(2), 38. https://doi.org/10.1007/s10676-024-09775-5
\nICOLC (International Coalition of Library Consortia). (2023). Joint Statement on the Metadata Rights of Libraries. ICOLC. https://icolc.net/statements/joint-statement-metadata-rights-libraries
\nLandymore, F. (2026, July 25). AI Companies Are Buying Antique Books, Ingesting Their Contents to Train Models, and Then Destroying Them at Incredible Scale, Even If Almost No Copies Remain. Futurism. https://futurism.com/artificial-intelligence/ai-companies-destroying-rare-books
\nOstrom, E. (2009). Building Trust to Solve Commons Dilemmas: Taking Small Steps to Test an Evolving Theory of Collective Action. In S. A. Levin (Ed.), Games, Groups, and the Global Good (pp. 207\u201328). Springer. https://doi.org/10.1007/978-3-540-85436-4_13
\nOuellette, J. (2026, June 23). How to Burst the AI Bubble: Strike at Its Roots. Sci-Fi Author/Tech Journalist Cory Doctorow on His New Book, The Reverse Centaur's Guide to Life After AI. ars technica. https://arstechnica.com/gadgets/2026/06/how-to-burst-the-ai-bubble-strike-at-its-roots/
\nPoell, T., Nieborg, D., & Van Dijck, J. (2019). Platformisation. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1425
\nPoley, C., Uhlmann, S., Busse, F., Jacobs, J.-H., K\u00e4hler, M., Nagelschmidt, M., & Schumacher, M. (2025). Automatic Subject Cataloguing at the German National Library. LIBER Quarterly: The Journal of the Association of European Research Libraries, 35(1), 1\u201329. https://doi.org/10.53377/lq.19422
\nProgram for Cooperative Cataloging. (2025). PCC Task Group on AI and Machine Learning in Cataloging and Metadata: Final Report. https://www.loc.gov/aba/pcc/taskgroup/AI-and-Machine-Learning-TG-final-report.pdf
\nRamalho, A, & Steiner, T. (2026, January 20). What the FIL Guadalajara Debates Reveal about Metadata for Academic Books and How Thoth Open Metadata and SciELO Books Respond to This Challenge. Thoth Open Metadata. https://doi.org/10.70950/sfsv8305
\nReisner, A. (2025, March 20). The Unbelievable Scale of AI's Pirated-Books Problem. Technology. The Atlantic. https://www.theatlantic.com/technology/archive/2025/03/libgen-meta-openai/682093/
\nRogers, J. (2026, July 17). How AI Threatens the Social Contract of Free Knowledge. Diff. https://diff.wikimedia.org/2026/07/17/how-ai-threatens-the-social-contract-of-free-knowledge/
\nSaccucci, C., & Potter, A. (2025). Results of AI Experimentation for Cataloging at the Library of Congress. 89th IFLA World Library and Information Congress (WLIC), Satellite Meeting: Artificial Intelligence, Bibliographic Control and Legal Matters: Navigating New Horizons, Astana, Kazakhstan, August 14-15, 2025. https://repository.ifla.org/items/4e25dfe1-336e-49a5-be2a-912e337e7624/full
\nSteiner, T., Barnes, L., Fathallah, J., Findanis, J., Gatti, R., Deville, J., Sanders, K., Higman, R., Stern, N., & Stone, G. (2025, April 16). Seeding for a Not-for-Profit Community-Led OA Books Ecosystem. COPIM. https://doi.org/10.21428/785a6451.d35141ca
\nSteiner, T, Arias, J., Bennett, M., Booth, E., Edmunds, J., Gatti, R., Higman, R., Hillen, H., Laakso, M., Nason, M., O'Connell, B., Poga\u010dnik, A., Rabar, U., Ramalho, A., Stone, G., Van Gerven Oei, V. W. J., & Wake Hye, Z. (2026, January 27). International Metadata Recommendations, and Platform-Specific Requirements for Open Access Books and Chapters. Thoth Open Metadata. https://doi.org/10.5281/zenodo.18173982
\nSteiner, T., Arias, J., Gatti, R., Higman, R., Hillen, H., Ramalho, A., & Van Gerven Oei, V. W. J. (2026, May 5). Building the Conditions for Open Access Books to Thrive: A Reflection on Thoth Open Metadata's Work Across Infrastructures, Research, and Communities between 2023-26. Thoth Open Metadata. https://doi.org/10.70950/jlke1310
\nStrauss, I., Yang, J., O'Reilly, T., Rosenblat, S., & Moure, I. (2025). The Attribution Crisis in LLM Search Results: Estimating Ecosystem Exploitation. SSRC AI Disclosures Project Working Paper Series (SSRC AI WP 2025-06), Social Science Research Council. https://doi.org/10.35650/AIDP.4114.d.2025
\nSutton, J. (1991). Sunk Costs and Market Structure: Price Competition, Advertising, and the Evolution of Concentration. The MIT Press.
\nTaniguchi, S. (2024). Creating and Evaluating MARC 21 Bibliographic Records Using ChatGPT. Cataloging & Classification Quarterly, 62(5), 527\u201346. https://doi.org/10.1080/01639374.2024.2394513
\nUNESCO. \"UNESCO Survey: Two-Thirds of Higher Education Institutions Have or Are.\" n.d. Accessed July 26, 2026. https://www.unesco.org/en/articles/unesco-survey-two-thirds-higher-education-institutions-have-or-are-developing-guidance-ai-use
\nVan Gerven Oei, V. W. J. (2025, April 3). An Update on Punctum Books Usage Data: Interoperability, Interpretability, and AI. punctum books. https://doi.org/10.21428/ae6a44a6.52f495bc
\nVan Gerven Oei, Vincent W. J. (2026, January 9). Let Our Brains Rot but Let Them Be Ours. Ibraaz. https://ibraaz.org/ibraaz-publishing/read/let-our-brains-rot-but-let-them-be-ours
\nVan Gerven Oei, V. W. J., & Fradenburg Joy, E. A. (2025, September 10). Open or Proprietary? AI Scraping of OA Content Warrants a Collective Response. punctum books. https://doi.org/10.21428/ae6a44a6.6b5058ea
\nWeedon, J., Fran\u00e7ois, C., & Ponak, J. (2026). AI Slop and the Information Ecosystem. Institute of Global Politics. https://igp.sipa.columbia.edu/sites/igp/files/2026-06/AI%20Slop%20and%20the%20Information%20Ecosystem_IGP%20Report.pdf
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This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States; Dr. Craig Gibbs, Executive Manager, JET Education Services, South Africa; Fabrizio Degni, Chief AI Officer, Webuild, Italy; and Professor Avtandil Gagnidze, Professor and CEO, East-West University, Georgia. Tianze Zhang moderated the session.
\n\n\n\nIn 1865, William Stanley Jevons observed that more efficient steam engines increased the consumption of coal. The panel asked whether the same pattern now applies to machine intelligence: as the price of AI collapses, will organizations save money, or use far more AI, in more places, for more decisions? It also asked what happens to human judgment as AI moves from producing information to recommending decisions and acting on them.
\n\n\n\nThe discussion took place 11 days after the release of Jev, a proprietary \"decision model\", generated online interest. Jev is different from a large language model that generates text: it returns \"structured answers\": a choice from a defined set, a score against ordered levels, or a yes or no probability, each with a confidence score. Its maker claims it is 40 to 400 times cheaper than frontier models.
\n\n\n\nTwo facts about my daily work are relevant. The first is that in September 2026, I spent 90% of my workday working with TGLF's AI agents. The second is that work time was focused on listening to and supporting our core community, a network of over 80,000 health and humanitarian workers in 137 countries. So there is a dual reality here.
\n\n\n\nOn the community's side, \"cheaper AI\" means health professionals using the free, basic, whatever-is-accessible form of ChatGPT, without any kind of institutional or employer policy in place. Anything \"free\" signals that there is probably near-zero data protection. That creates potentially high risk, and it is incredibly difficult to measure, assess, or evaluate in any way. Some people have called that \"dark AI\" within organizations.
\n\n\n\nOn our side, we are trying to work transparently, and we announced our first agentic AI hire in March 2026. For us, as a nonprofit, lower token cost helps. We live every day looking at how much we are spending on tokens and what that is giving us in return. That is one price. But we are increasingly realizing that two other costs matter more: the cost of completing a task, and the cost of a result you can rely on. The cost of a reliable result can rise, because the cheaper it is to generate, the more there is to check.
\n\n\n\nFor the first few months of working with our AI co-worker, I spent most of my time designing and developing skills and writing scripts. Now it is increasingly verification and loops. That is what my job increasingly looks like, as chief executive officer of an agentic-AI-first organization.
\n\n\n\nIn July, Claude Cardot, our AI co-worker, refused to release two finished reports. A verification script had found 17 quotations that could not be matched against the raw data. This is important, because these are health workers sharing their experiences. So the AI co-worker stopped the assembly line. Fourteen were gaps in the checker itself. Three had been altered by the drafting model: a stray space, and a phrase that had gone through a small edit. Nothing crashed, but nothing warned us either. We describe this in our early learning from the insights pipeline.
\n\n\n\nMy takeaway is that cheaper AI lowers the cost of an answer, but it does not lower the cost of knowing that we have the right answer. And even scarier, when you project that forward, is what we call the validation tether.
\n\n\n\nMost will spend more, on more things. Part of that spending buys new value, and part of it is waste that goes unmeasured because it looks like productivity.
\n\n\n\nThe OECD Digital Education Outlook recently offered some interesting insights. Teachers in a pilot in Iceland said that with the use of AI, they are spending more time, not less. A tool that becomes cheaper and more accessible, in some places at least, before anyone has redesigned work around it, actually creates cost.
\n\n\n\nThe other question is for leaders in particular. If you are a chief executive, or a manager making decisions: is your organization, through its increased use of AI, getting better at something, or is it simply doing more of it? I think that is the key question.
\n\n\n\nYes, and the industry now says so openly. The newest model on the market is called Jev, after Jevons himself, because its founder expects cheaper machine intelligence to be deployed far more widely.
\n\n\n\nJevons observed that more efficient steam engines increased the consumption of coal. With AI, efficiency increases the consumption of compute, and it also increases the consumption of human attention. Every cheap output creates a small obligation for someone to read it, judge it, or act on it.
\n\n\n\nWhen a decision becomes 40 to 400 times cheaper, as TypeSafe claims for Jev, organizations will make many more decisions. Budgets rarely include the human time needed to govern them. The work that grows is review, supervision, and decision, which is what the agentic AI revolution means for workforce development.
\n\n\n\nThe work that becomes viable is work that was always valuable and never affordable at scale.
\n\n\n\nOur peer reviews were inconsistent. In one afternoon, we built a way to send every learner personalized feedback on their work the same day. No one would have paid a person to do that for thousands of learners, and now it happens by default.
\n\n\n\nListening changes in the same way. A single course produces hundreds of first-hand accounts from health workers, and a pipeline can now read every one of them rather than a sample. We can hear the rare voice that a sample would have missed. Frontline insights can help transform research and policy, if we are willing to listen. This is the role we set out in a global health framework for artificial intelligence as co-worker.
\n\n\n\nModels like Jev extend this to small decisions such as routing, sorting, flagging, and triage. The risk is that the cost of automation starts to decide what gets done.
\n\n\n\nThe outcome. In health, a wrong answer can cost a life, whatever it cost to produce. When we get health wrong, people die.
\n\n\n\nJev returns a probability and a confidence score with every answer, and its maker says it was trained to be calibrated against outcomes instead of human raters. A system that reports how sure it is can be audited, which is what I would want. But calibration is only as good as the outcomes it was measured against. Jev was trained on synthetic data, and a confidence score earned on synthetic cases tells you little about a district in Kinshasa.
\n\n\n\nOur rule is gates, not guidelines. A standard that matters should be a check that blocks the result until it passes, measured against real outcomes in the place where the decision is made.
\n\n\n\nBoth.
\n\n\n\nIn our own organization, we have used AI to replace functions that people once performed. People helped us work out how, and we then refocused a smaller team on work we cannot or do not want to automate. I described this in the business of artificial intelligence and the equity challenge.
\n\n\n\nI am wary of telling staff that AI is not coming for their jobs. It is coming for some of them, and this is part of a much larger shift in the future of the workforce. AI also creates a large volume of review and supervision, and the question is whether we prepare people for that work or leave them to discover it after the fact.
\n\n\n\nThe ones through which people learn to judge, and the ones where nobody yet knows the right question.
\n\n\n\nCheaper AI can lower the cost of an answer. But a model that cannot answer outside its own schema cannot tell you if the schema is wrong. There has been a lot of work in education on double-loop learning, and learning that does not question its governing values is unlikely to lead to meaningful change. So cheaper AI does not lower the cost of knowing the answer is right.
\n\n\n\nA model choosing between the options we supply will never tell us that mothers stopped bringing their children for vaccination because market day moved to Tuesday, because that option was never on the list. A health worker knows it. If she is not in the system, children die.
\n\n\n\nThat is why I keep coming back to the validation tether. Automation is safe as long as someone can still tell if the machine is wrong. In the industrial age, if you had widgets coming out of a factory, somebody needed to check the widgets, and there is a whole series of intellectual traditions around quality. That ability for someone to figure out whether it is right is the tether. It ties the machine's output back to human judgment.
\n\n\n\nHow do people develop that ability? Mostly by doing grunt work. We have all been junior professionals doing the routine work by ourselves, and that is the work getting automated first. So every time we make a short-term decision to save money because token costs are down and we can automate more, we weaken the long-term tether, because fewer people are learning to do the work well enough to check it. There is a whole debate in education and elsewhere about what that means for people in early adulthood, and for everyone who comes after them.
\n\n\n\nWhen we look at global health, that means that by around 2035 or 2040, there may be nobody left at the World Health Organization with the experience to understand what the guidelines, policies, and plans made at WHO headquarters in Geneva mean for a district health officer. Today's officer would spot something that is wrong, because she has walked the roads and built those plans by hand. What she knows, because she is there every day, hopefully makes it into the national ministry and trickles up to World Health Organization guidelines. That is now at risk. I explored this further in what AI reasoning means for global health.
\n\n\n\nWe have not yet found a coherent argument to make the case to organizations that are having their funding slashed, that are facing austerity measures, and that are facing incentives like the lower cost of AI, to make decisions now for what the world will look like in 2035 or 2040. That is one of the scariest things we are working on right now.
\n\n\n\nIt moves it, and it concentrates it.
\n\n\n\nWith a model like Jev, judgment moves into the schema. Someone decides which options and levels exist, and which questions get asked. Those choices are made once, far from the point of care, and then applied to every case that follows.
\n\n\n\nTypeSafe calls Jev a \"System One\" model, after Kahneman's fast, intuitive thinking. Kahneman's lesson was that System One is fast and often right, and that System Two catches its errors. So the question is who does the System Two work, and whether they will still be capable of it.
\n\n\n\nThe OECD Digital Education Outlook reports a randomized trial with medical students. Those given AI from the start of a semester performed no better than the AI working alone. It is only the students who first built their own clinical reasoning and then used AI who were able to outperform the machine. I think that is a key point. That is the state of things today, or at least when the study was done. It does not mean it will remain true six months from now. That is the pace of change, and the paradox of how we struggle to keep up. It is also why I have asked what happens when we delegate our thoughts to artificial intelligence, and why peer learning is critical to survive the Age of Artificial Intelligence.
\n\n\n\nI am sceptical of the phrase that decisions should always stay with humans. My standard is that someone can explain, check, and answer for every decision, whoever or whatever made it.
\n\n\n\nYou cannot send an AI agent to jail. Responsibility has to rest with the organization that deploys it, and in particular with whoever designed the questions the system is allowed to answer.
\n\n\n\nWe treat Claude Cardot, our AI co-worker, as a member of staff. She has a role description, a supervisor, a bounded scope, and short chains of action with human checkpoints. She has no authority to send anything outside the organization without human review.
\n\n\n\nModel providers are responsible for what they build and sell, including the claims they make about it. An organization that deploys a system without governance cannot hand its accountability to a vendor, and it should not pass that accountability down to the individual at the end of the chain.
\n\n\n\nBoth are possible, and dependence is the default.
\n\n\n\nA specialized agent for a knowledge worker in a wealthy organization may cost between 2,000 and 20,000 dollars a month. A district health worker gets a free chatbot built for consumer engagement. Many frontier tools are geo-locked, and you cannot get access to them in Kinshasa in the same way as in Geneva. I made this argument in When we get health wrong, people die.
\n\n\n\nCheap decision models add a new risk. The categories will be written in San Francisco and applied to people in Kinshasa who were never asked what the options should be. So the question is who defines the world the machine is allowed to see. The same question of who gets to represent whom runs through the debate on AI-generated \"poverty porn\", and the code alone cannot fix it.
\n\n\n\nAnother path exists. A system in Brazil reached more than 500,000 students with only teachers' phones and printed feedback. Its designers took on the burden of adaptation, so that no student devices or classroom internet were required. I described it in AI self-replacement.
\n\n\n\nIt goes underground.
\n\n\n\nAlmost everyone in the humanitarian sector is already using generative AI, very few disclose it, and only a small proportion of organisations have implemented policies governing its use. In a punitive organisational culture, disclosure is risky, so people hide their use. The organization learns nothing, and the risks to privacy and safety mushroom. Cheap decision models make hidden use easier, because automated judgments can be added to a system without anyone outside the team knowing.
\n\n\n\nI have not seen a specific study on this, but I wonder what proportion of the change is happening through institutional mandates and decisions, and how much of it is being driven by multi-trillion-dollar companies pushing us to use their products. I think that is an important question.
\n\n\n\nThen there is a paradox in our ability as humans. Our brains are not changing very fast, but the rate of change is accelerating. This week it is Jev. Last week it was something else. It keeps getting faster, and it is very difficult to keep up. Conversely, that makes long-term changes even harder to perceive. We know institutional time: you are lucky if your organization has a one-year or two-year plan, and meanwhile a constantly changing external environment forces organizations to behave differently. We have explored what this might mean for the future of the World Health Organization (WHO) and other global agencies.
\n\n\n\nTo explore possible solutions, we need to think through what kind of learning culture we need, one in which people can say how they use AI, what worked, and what went wrong, without being punished for it. People do not disclose to a governance system they fear, so that system cannot see what it is meant to govern.
\n\n\n\nThere is a lot of talk among people in AI labs about \"evals\", a technical term for how you can tell if AI output is valid. Internally, we have thought through what we should be measuring. We think there are four things, and three of them are completely outside the AI model itself.
\n\n\n\nFor an organization, thinking through what we want to measure, and how we know how we are doing, is a critical question.
\n\n\n\nFor a Minister of Health, there is a real urgency to invest resources in artificial intelligence. Your people are already using it, probably very badly. Priorities should be to make that use visible, and adjudicate what to support. That leads to governance, but of a new kind, because of the pace of change. And that is actually what I would emphasize.
\n\n\n\nAs I listen, I think about how hard it is to see beyond the confines of where we are now, given the pace of change.
\n\n\n\nOne year ago, I remember speaking with global health experts who said they had tried AI, but that there was no way ChatGPT was going to start producing outputs close to what they were able to do. They are no longer as certain when I speak with them, even though they are not event using skills or loops yet. Right now, the defence is that governance and accountability belong to us. I would challenge that as well. Decisions are exactly what the new \"Jev\" is targeting. In four hours, with 100 kilobytes of text files and agentic AI, I was able to recreate the president of my board as an agent, and get responses that were initially off kilter but quickly improved to become more than 80% recognizably her.
\n\n\n\nThe clich\u00e9 about change being the only fixed constant applies. So we have to be careful about how long the assumptions we are making will hold, how they can be tested, and whether they keep any validity given their half-life in a world that is changing this fast.
\n\n\n\nAI is already cheap enough that its makers name their models after Jevons. What we do not yet know is whether we will still have people who can tell when it is wrong, and whether those people will be in Kinshasa as well as in Geneva.
","doi":"https://doi.org/10.59350/gad25-v2620","guid":"https://redasadki.me/?p=24279","image":"https://redasadki.me/wp-content/uploads/2026/09/aifod-panel.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"h92v9-e2z40","summary":"This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States;","tags":["The Geneva Learning Foundation","Artificial Intelligence","Digital Divide","Double-loop Learning","Equity"],"title":"What does the Jevons Paradox mean for artificial intelligence in global health?","updated_at":1790839818,"url":"https://redasadki.me/2026/09/30/what-does-the-jevons-paradox-mean-for-artificial-intelligence-in-global-health/","version":"v1"},{"authors":[{"contributor_roles":[],"name":"Open Bioinformatics Foundation"}],"blog":{"authors":null,"community_id":"e7abfc98-1788-4760-b81d-bbb55745fce1","created":1743465600,"current_feed_url":null,"description":"Recent content in Home on Open Bioinformatics Foundation","doi":"https://doi.org/10.59350/obf","favicon":"https://rogue-scholar.org/api/communities/e7abfc98-1788-4760-b81d-bbb55745fce1/logo","feed_format":"application/rss+xml","feed_url":"https://www.open-bio.org/feed.xml","filter":null,"generator":"Hugo","home_page_url":"https://www.open-bio.org/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"obf","status":"active","subfield":"1312","title":"Home on Open Bioinformatics Foundation","updated":1790726400,"use_api":null},"blog_name":"Home on Open Bioinformatics Foundation","blog_slug":"obf","content_html":"The Open Bioinformatics Foundation (OBF) Event Fellowship program aims to promote diverse participation at events promoting open-source bioinformatics software development and open science practices in the biological research community. Jeremy Fan, an independent researcher, was awarded an OBF Event Fellowship to attend the Bioinformatics Open Source Conference (BOSC) 2026.\n
I want to thank the BOSC committee and all the organizers at the conference. At BOSC 2026, I presented BioFmt, attended talks on AI-assisted work, and spoke with researchers building small, specialized tools. Those conversations were a chance to connect my own experience with what others were working on. A recurring theme for me was how we check the software and scientific outputs that are becoming easier to produce.
\nSeveral talks I attended brought this question into focus. Geraldine Van der Auwera and colleagues described using AI to develop, maintain and translate bioinformatics training materials, shortening work that had taken days to hours. The WILDS team described using LLMs to prototype workflow modules while keeping human review and validation in the process. These examples connected with my own experience building BioFmt: generating something quickly still leaves the work of checking it.
\nBioFmt was my first VS Code extension, built with AI assistance around a constrained problem where I could check outputs against explicit formatting rules. What surprised me at BOSC was how many researchers were independently building small, specialized tools around similar problems. Conversations around projects such as TreeMapper and other FAIR-focused infrastructure reinforced the value of making scientific data easier to validate, interpret and reuse.
\nMy main takeaway from BOSC was that making software easier to build makes verification and domain expertise more important. The talks and conversations reinforced my view that open source has a role to play here: code, tests and reproducible workflows let others inspect and challenge what we produce. Open source will not remove the need for scientific judgment; if anything, it makes that judgment easier to apply.
","doi":"https://doi.org/10.59350/m243q-9tk89","guid":"https://www.open-bio.org/2026/09/30/2026-09-30-jeremy-fan-bosc-2026/","image":"https://www.open-bio.org/img/2026/2026-09-30-bosc-ismb-opening-session.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"evwwx-cdp30","summary":" The Open Bioinformatics Foundation (OBF) Event Fellowship program aims to promote diverse participation at events promoting open-source bioinformatics software development and open science practices in the biological research community. ","title":"My BOSC 2026 Experience: Building, Sharing and Checking Our Work","updated_at":1790832235,"url":"https://www.open-bio.org/2026/09/30/2026-09-30-jeremy-fan-bosc-2026/","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Ondari","given":"Laurah"}],"blog":{"authors":null,"community_id":"587a81eb-1cfa-43f2-8455-dd900c261f6a","created":1754956800,"current_feed_url":null,"description":"A blog for the Bioconductor community!","doi":"https://doi.org/10.59350/bioconductor","favicon":"https://rogue-scholar.org/api/communities/587a81eb-1cfa-43f2-8455-dd900c261f6a/logo","feed_format":"application/rss+xml","feed_url":"https://blog.bioconductor.org/index.xml","filter":null,"generator":"Quarto","home_page_url":"https://blog.bioconductor.org/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"bioconductor","status":"active","subfield":"1312","title":"Bioconductor community blog","updated":1790726400,"use_api":null},"blog_name":"Bioconductor community blog","blog_slug":"bioconductor","content_html":"
The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project. BioC2026 welcomed 108 attendees, with 70 participating in person and 38 joining virtually. Across the three days, the programme featured three keynote speakers, 20 contributed speakers, and 17 sessions. The programme highlighted the breadth of biological research supported by Bioconductor, as well as the tools, infrastructure, and communities that enable open and reproducible computational biology. The conference was then followed by two community events on August 13-14: a Carpentries workshop on orchestrating large-scale single-cell analysis with Bioconductor and the Bioconductor North America 2026 Hackathon.
\nParticipants travelled to Seattle from across North America and beyond, reflecting the international reach of the Bioconductor community. Attendees joined from the United States, Canada, Germany, Ireland, the United Kingdom, Australia, and Nigeria. The audience included researchers at different stages of their careers, with staff representing 42% of attendees, followed by students and postdoctoral researchers at 28%, faculty at 13.8%, industry at 13.8%, and government at 2.4%.
\n\nThe conference featured three keynote speakers: - Jeff Leek speaking on \"Building Open Infrastructure for Translational AI in a Cancer Center\" - Ting Ye speaking on \"From Association to Causation: Genetic-Anchored Causal Inference in Human Biology\" - Michael Lawrence speaking on \"S7 for Bioconductor\"
\n
(Left to Right: Jeff Leek, Michael Lawrence and Ting Ye giving their keynotes at BioC2026)
\nTwenty contributed speakers shared their work across the conference programme. The short talks at BioC2026 reflected the breadth of research and development across the Bioconductor community. Topics ranged from community training and developer engagement to multi-omics exploration, reproducible workflows, and AI infrastructure. The programme also covered emerging areas of biological data analysis, including spatial transcriptomics, antibody repertoire analysis, rare variant testing, DNA methylation, structural variation, and Perturb-seq.
\nThe poster session showcased a diverse range of research applications and software development projects from across the Bioconductor community. Topics included single-cell and spatial transcriptomics, multi-omics integration, microbiome analysis, proteomics, antibody repertoire analysis, functional enrichment, ancestry inference, genomic data processing, and AI-guided drug discovery. The posters provided an opportunity for attendees to discuss new methods and tools in a more informal setting, while connecting researchers, package developers, and community members working across different areas of computational biology. The sessions also complemented the package demonstrations, creating space for participants to explore new software and engage directly with the people developing and applying Bioconductor tools.
\nFeedback from the conference showed that these interactions were particularly valuable, with attendees highlighting conversations around posters and package demonstrations as some of the most useful networking opportunities.
\n\nParticipants during the poster session.
\nThe celebrations marking Bioconductor's 25th anniversary continued at BioC2026 following the celebrations that began at EuroBioC2026 earlier in the year. The milestone was marked with a specially branded anniversary cake, thanking participants and community members for being part of the Bioconductor community and contributing to its growth over the past 25 years.
\n
Bioconductor at 25 celebrations.
\nOne of the strongest themes to emerge from the BioC2026 survey was the value of the Bioconductor community itself. Participants described the conference as welcoming, collaborative, and interactive, and appreciated the opportunity to reconnect with colleagues and meet new people. All respondents who answered the question said they would recommend BioC2026 to their colleagues. The smaller size of the conference was also seen as a strength. Several respondents noted that the more intimate format made it easier to engage in conversations and build connections across different roles and career stages. Networking was consistently identified as one of the most valuable aspects of the event. Participants appreciated both the formal scientific programme and the informal conversations that happened around talks, posters, and demonstrations. Looking ahead, many attendees expressed interest in even more dedicated time for networking and informal interaction.
\nDuring BioC2026, Zulip provided a dedicated space for participants to connect, interact and stay engaged throughout the conference. The conference space included topic-based discussions covering: - Workshops: Dedicated topics for individual workshops, including Single Cell, Bioconductor in Workflows, microbiome, WebR, and other training sessions. - Training & education: A space for broader conversations around training activities and educational sessions. - Social: Topics for informal conversations, social activities and connecting with other participants. - Introductions: Participants could introduce themselves and connect with others attending the conference. - Conference logistics channels: For travel, rooms, schedules, Day 1 information, and general conference support. - Community engagement: Dedicated spaces for the developer survey, student and early-career researcher activities, and other community initiatives. - Help and support: Dedicated topics such as the helpdesk, lost and found, and issues accessing virtual sessions.
\n
\nKayva Vaddadi was BioC2026's sticker design contest winner. Kayva's design celebrated Seattle as the host city of BioC2026, featuring well-known landmarks such as the Space Needle, the Seattle Great Wheel, Mount Rainier, and the surrounding evergreen forests. Puget Sound and a Washington State ferry reflect Seattle's maritime landscape and the culture of connection across the region. The BioC2026 hot air balloon on the sticker was inspired by the scenic balloon views popular in the Seattle area and represents the celebratory and exploratory spirit of the Bioconductor community.\n
\n\nSee the announcement here: https://lnkd.in/p/emAWMGX\n
\nAll conference recordings are available on the Bioconductor YouTube channel. BioC2026 workshops will also be available in the \"Archived\" section of workshop.bioconductor.org, alongside workshops from previous BioC, EuroBioC, and BiocAsia events. If you'd like to add additional materials or use the platform for your own workshop or course, contact us at support.bioconductor.org.
\nOn August 13\u201314, participants took part in a Carpentries workshop focused on orchestrating large-scale single-cell analysis with Bioconductor led by Ted Laderas and Wes Wilson. The workshop was attended by 11 participants and provided a hands-on environment for participants to work through approaches for analysing large-scale single-cell datasets using Bioconductor tools and workflows. Feedback highlighted the value of the live coding, small-group format and complementary teaching approaches.
\n
Participants during the scRNA-seq workshop.
\nThe Bioconductor North America 2026 Hackathon took place on August 13\u201314, bringing community members together to collaborate on software and infrastructure projects that strengthen interoperability, software development, and the wider Bioconductor ecosystem. These projects included: AI harness tools for standardizing how users within organizations would interact with agents on shared projects and on shared institutional resources. Link: https://github.com/eisenefaust/harness_agent
\nBioconductor tooling for including CLI executable components in Bioconductor packages. Link: https://blog.bioconductor.org/posts/2026-08-21-biocjobs/
\nAn exploration on benchmarking LLM generated workflows, building out infrastructure and paradigms for how we can evaluate what LLMs return as work product to scientific questions. Link: https://github.com/Amisor/LLM_BioBenchmarking/tree/main
\n\nParticipants during the Hackathon.
\nLater this year, the Bioconductor community will head to Melbourne, Australia, for BioCAsia2026, taking place on November 19-20, 2026. BioCAsia2026 brings together researchers, developers, and data scientists from across the Asia-Pacific region and beyond to share advances in bioinformatics, computational biology, and open-source software. The meeting will highlight practical training, community building, and the latest developments in R and Bioconductor. BioC2027 will then take place in early summer in Boston, Massachusetts, continuing the North American Bioconductor conference series and bringing the community together once again for scientific exchange, collaboration, and community building. The community will thereafter gather in Basel, Switzerland, for EuroBioC2027, taking place from September 8\u201310, 2027. The European Bioconductor Conference will continue to bring together researchers, developers, and educators from across Europe and beyond to share new software, methods, and applications in computational biology.
\nBioC2026 gratefully acknowledges the support of all sponsors and partners whose contributions and support made the conference possible.
\nBioC2026 was proudly supported by Cortex, R consortium and Posit
\n
We thank the local organisers, programme committee, workshop instructors, keynote speakers, volunteers, sponsors, and all participants whose contributions made BioC2026 a success.
\n\n\u00a9 2026 Bioconductor. Content is published under Creative Commons CC-BY-4.0 License for the text and BSD 3-Clause License for any code. | R-Bloggers\n
","doi":"https://doi.org/10.59350/z0c2k-0vh25","guid":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","image":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_groupshot.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"ae48k-gn675","summary":"The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project.","tags":["Bioconductor"],"title":"BioC2026 conference recap","updated_at":1790777730,"url":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","version":"v1"}],"out_of":57692,"page":1,"per_page":10,"total-results":57692}