{"found":50051,"hits":[{"document":{"abstract":"*depending strongly on who you are buying from, what the topic and preferred venue are and what authorship slot you want","archive_url":null,"authors":[{"contributor_roles":[],"family":"Richardson","given":"Reese"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"engineeringAndTechnology","community_id":"9fd043ee-3a61-4c8f-8cd1-86bd7c4104c8","created_at":1721839864,"current_feed_url":null,"description":"Case studies in scientific reproducibility","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/40570e0b-b289-4145-9c60-6fb881efaa45/logo","feed_format":"application/atom+xml","feed_url":"https://reeserichardson.blog/feed/atom","filter":null,"funding":null,"generator":"WordPress.com","generator_raw":"WordPress.com","home_page_url":"https://reeserichardson.blog","id":"09a10db2-1049-40ae-ad18-952788601632","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://mastodon.social/@three_body_problem","prefix":"10.59350","registered_at":1728997684,"relative_url":null,"ror":null,"secure":true,"slug":"reeserichardson","status":"active","subfield":"1802","subfield_validated":null,"title":"Reese Richardson","updated_at":1777452375.276094,"use_api":false,"use_mastodon":false,"user_id":"b4591919-6189-40a9-a853-2d228796a743"},"blog_name":"Reese Richardson","blog_slug":"reeserichardson","content_html":"

*depending strongly on who you are buying from, what the topic and preferred venue are and what authorship slot you want.

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For the uninitiated: paper mills are illicit organizations that sell services to boost an academic\u2019s publication profile. Although a paper mill can operate by a variety of business models, the prototypical paper mill mass produces fraudulent scholarly manuscripts, sells authorship slots on those manuscripts and colludes with editors at target journals to get those manuscripts published.

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I and others researching paper mills are frequently asked how much these services cost. In part to make answering this question easier, myself and my colleagues Spencer Hong and Anna Abalkina created BuyTheBy, to date the largest structured dataset of paper mill advertisements. BuyTheBy v1.0 contains price data from 18,710 advertisements made by seven paper mills operating out of seven different countries.

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Check out the preprint on arXiv:

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BuyTheBy: A dataset of 18,710 text-based paper mill advertisements with 51,812 timestamped prices (10.48550/arXiv.2604.24576)

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The study of paper mills and similar businesses operating in the market for academic and education fraud services is frustrated by the lack of market price data on their various offerings. Here, we assemble BuyTheBy, a large, annotated dataset of timestamped, text-based paper mill advertisements from seven businesses operating out of seven different countries. The dataset consists of 18,710 individual advertisements, of which 15,839 have prices listed. Among these there are 20,598 positions listed as for sale on 5,567 unique products in 14 different product categories with 51,812 timestamped price data points. We perform elementary analysis of this dataset to demonstrate its utility for quantitative understanding of markets for academic fraud services and suggest future use cases.

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And the dataset itself on Zenodo:

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BuyTheBy \u2013 An annotated dataset of paper mill advertisements with price data (10.5281/zenodo.19684277)

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There are many useful things you can do with this dataset and interesting tidbits contained therein (for ideas of analyses that could be performed here and what you mind find, I defer to the preprint and coverage by Nature, Science, Retraction Watch, C&EN and Times Higher Education). I\u2019ll use this post to take at the recurrent question that launched the development of this dataset: how much does a paper mill product actually cost?

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The prices offered for authorship slots on articles vary dramatically, even within a single paper mill\u2019s catalog. For instance, the advertisements we processed from an Uzbekistani paper listed prices between $100 and $2000 for first authorship. Clearly, paper mills will offer a variety of products at a variety of price points. Prices also varied wildly between paper mills; while our Indian paper mill was not selling any article authorship positions at a price greater than $200, our Russian paper mill was selling positions for as much as $5,600.

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Boxplots showing distributions of prices for authorship slots on articles for the seven paper mills included in BuyTheBy v1.0 (see Figure 3 in the preprint).
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Then the answer is, predictably, it depends. If this answer doesn\u2019t cut it for you, you can look past all limitations and conditionals for a single central estimate, which the dataset will provide: the median first authorship slot offered by these seven paper mills goes for about $800. Based on median listed prices, buying every slot on a five-author manuscript probably costs around $3,000.

I hope that one way others use ByTheBy is by mapping titles offered in these advertisements to their eventually-published products. For a demonstration of this, check out this impressive piece of work by Anna Abalkina, Marie Kune\u0161ov\u00e1, Yagmur Ozturk and Solal Pirelli, for which they tracked more than 1700 advertised titles to articles that were eventually published in conference proceedings:

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Opening Pandora\u2019s box: Paper mills in conference proceedings

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Paper mills are a growing threat to the integrity of science, yet their penetration in conference proceedings remains underexplored despite conferences being more important than journals in some scientific subfields. This study aims to identify papers in conference proceedings whose titles have been offered for sale on social media platforms. We collected more than 4,000 unique publication offers from more than 200 social media channels and used semi-automated methods along with human assessment to match offers with papers published in IEEE conference proceedings. We identified 1,720 papers in 286 IEEE conference proceedings, accounting for up to 23.51% of an individual conference. These problematic papers are co-authored by more than 6,500 researchers from over 3,500 affiliations in 55 countries. The identified papers demonstrate collaboration anomalies, high diversity of affiliations per paper, citation manipulation, a predominance of six-author papers, and content-based irregularities. Our findings show that paper mills are a large, organized, and often public market that commercializes scientific misconduct, not limited to papers, but infiltrating multiple parts of the research ecosystem.

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Both of these manuscripts demonstrate that paper mills offer a variety of different products, from authorship slots on journal articles to authorship slots on conference proceedings to editorship on textbooks to bogus prizes to citation boosting to \u201cdesign patents\u201d (which are actually not patents in the jurisdictions being advertised, more on that here). More generally, they demonstrate that although paper mills operate largely in secret, they conduct enough business out in the open that we do not have to content ourselves with just guessing.

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The header image for this post is an illustration from a manuscript of Nicole Oresme\u2019s 14th century translation of Aristotle\u2019s Ethics, Politics, and Economics.

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","doi":"https://doi.org/10.59350/9cshf-6tj25","funding_references":null,"guid":"http://reeserichardson.blog/?p=4758","id":"980d2b13-d028-47b3-b6f4-a3efe12e262e","image":"https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=1024","images":[{"height":"343","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=1024","srcset":"https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=1024, https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=2048, https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=150, https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=300, https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=768, https://reeserichardson.blog/wp-content/uploads/2026/04/260409_article_prices_by_position_dynamic.png?w=1440","width":"1024"},{"alt":"an illustration","src":"https://commons.wikimedia.org/wiki/File:Medieval_market.jpg"}],"indexed":true,"indexed_at":1777371849,"language":"en","parent_doi":null,"published_at":1777369674,"reference":[],"registered_at":0,"relationships":[],"rid":"fxkr2-q5x09","status":"active","summary":"\n *depending strongly on who you are buying from\n\n,\n\n what the topic and preferred venue are\n\n\n and what authorship slot you want.\n\nFor the uninitiated: paper mills are illicit organizations that sell services to boost an academic\u2019s publication profile.","tags":["Paper Mills"],"title":"A scientific paper will cost you around $800*","updated_at":1777369674,"url":"https://reeserichardson.blog/2026/04/28/a-scientific-paper-will-cost-you-around-800/","version":"v1"}},{"document":{"abstract":"Science is often described as a universal enterprise, but for many researchers in low- and middle-income countries (LMICs), especially across Africa, it does not feel universal at all. It feels fragmented, under-resourced, and difficult to enter on equal terms. The barriers are not only financial or technical.","archive_url":null,"authors":[{"contributor_roles":[],"family":"Olufemi","given":"Seun"}],"blog":{"archive_collection":22124,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22124/20231105103706/","archive_timestamps":[20231105103706,20240505132151,20241105103657,20250505103918],"authors":null,"canonical_url":null,"category":"humanities","community_id":"aeaafcbb-94b5-477a-a89f-8fba5925e926","created_at":1673568000,"current_feed_url":"https://upstream.force11.org/atom/","description":"The community blog for all things Open Research.","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/b56ef314-34f7-4c7f-b0e2-d0bf13bfe83b/logo","feed_format":"application/atom+xml","feed_url":"https://upstream.force11.org/atom-complete/","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.25","home_page_url":"https://upstream.force11.org","id":"e3952730-ffb7-4ef9-b4a5-6433d86b2819","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://scicomm.xyz/@upstream","prefix":"10.54900","registered_at":1729244339,"relative_url":null,"ror":null,"secure":true,"slug":"upstream","status":"active","subfield":"1802","subfield_validated":null,"title":"Upstream","updated_at":1777452815.561994,"use_api":true,"use_mastodon":false,"user_id":"08014cf6-3335-4588-96f4-c77ac1e535b2"},"blog_name":"Upstream","blog_slug":"upstream","content_html":"

Science is often described as a universal enterprise, but for many researchers in low- and middle-income countries (LMICs), especially across Africa, it does not feel universal at all. It feels fragmented, under-resourced, and difficult to enter on equal terms. The barriers are not only financial or technical. They are built into the infrastructure, incentives, and systems that shape who gets to participate fully in research and who is left working around the edges. As artificial intelligence (AI) begins to reshape scientific work, it offers both a new opportunity and a new test: whether emerging tools will help repair those inequities or deepen them.

Artificial intelligence offers a powerful opportunity to mend this fragmentation. It can help researchers work around barriers that have long limited participation. But that promise is not guaranteed. AI is still shaped by the same inequalities embedded in the broader scientific system, and without more deliberate investment in access, infrastructure, and inclusion, it may end up reinforcing the very gaps it seems to address.

As part of my work with the Bioinformatics Outreach Nigeria (BON), a community-driven effort to build capacity in bioinformatics and open science, we conducted a survey of 212 respondents in our community. This survey offers insights into a few of the systemic issues facing LMIC-based scientists and how AI affects these dynamics. 

The everyday barriers of scientific work

For researchers in LMICs, scientific work and access to learning opportunities often look very different from what researchers in well-resourced countries experience. In our recent survey, one key baseline insight was the overall observation that LMIC-based bioinformatics researchers perceive their work as difficult, with 50% describing it as very difficult and another 33.5% as somewhat difficult. This is not difficult to understand as modern bioinformatics research relies on many factors that can be out of reach of many LMIC-based scientists: reliable internet, computational power, and stable electricity, etc. Slow connections turn simple tasks into ordeals, while power outages disrupt workflows entirely, as this makes research learning processes almost impossible, and research processes very daunting. 

Just as important, even when infrastructure hurdles are cleared, knowledge itself stays locked behind paywalls. Subscriptions to major journals are often unaffordable, and open access publishing frequently brings high article processing charges that institutions cannot cover. This creates a paradox: researchers are expected to contribute to global science while being largely excluded from its latest developments. The result is skewed research agendas that prioritize high-income contexts, reduced visibility for local knowledge, and a persistent cycle where LMIC scientists remain primarily consumers rather than producers.

Beyond the difficulty of conducting research, access to training and mentorship forms another major bottleneck. The same survey showed strong demand for practical support, with the majority of respondents prioritizing workshops, requesting one-on-one training, and seeking online courses or tutorials. These types of structured opportunities for advanced skills are scarce in LMICs, and many online resources assume high-bandwidth access or prior expertise that beginners may lack. It further highlighted lack of training or knowledge (24.5%) and lack of access to resources or equipment (23.1%) as the top challenges when using bioinformatics tools. Without localized guidance, aspiring scientists often navigate complex fields in isolation. Community initiatives like BON address this by offering workshops, hands-on sessions, and mentorship tailored to real constraints. These efforts show how quickly capacity can grow when barriers drop. Yet they typically run on volunteer energy and limited funding, operating at the margins rather than as part of sustained national systems.

The survey also pointed to a deeper structural problem: the incentives surrounding scientific work are often poorly aligned with openness, collaboration, and capacity building. Systems reward high-impact publications but do little to recognize openness, data sharing, collaboration, or capacity building. Concerns about misuse or lack of credit further discourage open practices, even among motivated researchers. Together, infrastructure gaps, knowledge barriers, training shortages, and poor incentives reinforce one another, producing an ecosystem where global science participation stays uneven. 

AI as Opportunity and Risk

Artificial intelligence offers a powerful opportunity to ease some of this fragmentation. AI can democratize knowledge by summarizing complex research papers, translating content across languages, and adapting formats for easier understanding, helping researchers in LMICs stay current with global developments when access is uneven. In resource-scarce environments like those highlighted in our survey, AI can act as a personalized tutor, providing real-time guidance through coding challenges, analytical methods, and bioinformatics workflows.

More specifically, what makes AI especially important in this context is that it maps so clearly onto the challenges our survey identified. When research is difficult because infrastructure is weak and access is uneven, AI can help lower some of the friction around discovery, analysis, and technical work. When training and mentorship are hard to access, it can offer a form of immediate, practical support. And when scientific incentives do little to reward openness or collaboration, AI can at least make some of the labor behind documentation, metadata, and reproducibility easier to manage. It cannot fix these deeper structural problems by itself, but it can help relieve some of the pressure they place on researchers. Yet these benefits are not automatic. AI systems trained predominantly on data from high-income countries risk embedding biases that perform poorly in LMIC contexts, potentially widening rather than closing existing gaps. Uneven access to advanced tools could deepen inequalities, and concentrated control by a few institutions or companies might centralize power in new ways. The real challenge lies in ensuring AI is deployed inclusively, with models developed or fine-tuned using diverse, locally relevant data.

Rebuilding science as a truly global enterprise will require more than technological fixes. It will require deliberate investment in reliable infrastructure, equitable models for knowledge access, institutionalized training programs, and incentives that reward openness and collaboration. Grassroots efforts like BON show that focus on meaningful change can begin locally through targeted mentorship and community programs. Scaling these efforts will require sustained funding, policy integration, and institutional support.

The fragmentation of science in LMICs is not inevitable. It reflects deliberate choices about infrastructure, access, training, and reward systems that have left too many LMIC-based researchers working at the margins. AI will not solve these problems on its own, but will create a real opportunity to ease some of the burdens they impose. The question is whether these tools will be developed and deployed in ways that make science more equitable, or whether they will simply make an already uneven system more efficient for those who are already well served by it. 

","doi":"https://doi.org/10.54900/69s4r-kd236","funding_references":null,"guid":"https://doi.org/10.54900/69s4r-kd236","id":"e3796049-a565-4297-b1c9-b2ea8038477d","image":"https://images.unsplash.com/photo-1596220023937-06cd0679a7aa?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDI3fHxhZnJpY2ElMjBkb2luZyUyMHNjaWVuY2UlMjB3aGVuJTIwaXRzJTIwbm90JTIwYnVpbHQlMjBmb3IlMjB5b3V8ZW58MHx8fHwxNzc3MTQyMTAwfDA&ixlib=rb-4.1.0&q=80&w=2000","images":[],"indexed":true,"indexed_at":1777366490,"language":"en","parent_doi":null,"published_at":1777363208,"reference":[],"registered_at":0,"relationships":[],"rid":"pfjdf-c3507","status":"active","summary":"Science is often described as a universal enterprise, but for many researchers in low- and middle-income countries (LMICs), especially across Africa, it does not feel universal at all. It feels fragmented, under-resourced, and difficult to enter on equal terms. The barriers are not only financial or technical.","tags":["Insights","Thought Pieces"],"title":"Doing Science in the Age of AI, when the system isn\u2019t built for you","updated_at":1777363208,"url":"https://upstream.force11.org/doing-science-in-the-age-of-ai/","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Openness","given":"&"}],"blog":{"archive_collection":24082,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":[{"name":"Leiden Madtrics","url":null}],"canonical_url":null,"category":"socialScience","community_id":"d8304840-75c2-4164-bc37-ec879c4f065b","created_at":1682899200,"current_feed_url":null,"description":"Leiden Madtrics","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/atom+xml","feed_url":"https://www.leidenmadtrics.nl/atom/","filter":null,"funding":null,"generator":"Other","generator_raw":"Other","home_page_url":"https://www.leidenmadtrics.nl/","id":"a0920819-e194-4514-bca4-5f0837e10c51","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1728549179,"relative_url":null,"ror":"https://ror.org/027bh9e22","secure":true,"slug":"leidenmadtrics","status":"active","subfield":"1804","subfield_validated":null,"title":"Leiden Madtrics","updated_at":1777451929.935049,"use_api":null,"use_mastodon":false,"user_id":"ae88df6b-e1cf-4743-86a8-c032659cf5d2"},"blog_name":"Leiden Madtrics","blog_slug":"leidenmadtrics","content_html":"

The need for open research information

Research information \u2014 which describes research outputs, activities, and actors \u2014 is essential to understand how science operates, how knowledge is produced, and how research systems evolve. Despite its importance, much of this information remains locked in closed and proprietary systems, creating barriers to its use. These barriers restrict the possibility to study the research system in a comprehensive way, to make well-informed decisions about its governance, and to ensure equity in information access and decision-making.

The Information & Openness focal area at CWTS was founded in 2023 to address these challenges by working to advance the creation and use of open research information (ORI) that is free to access and reuse without restriction. In this blogpost, we outline a strategic plan, developed to guide our work toward this goal over the next three years.

The need for strategic action at CWTS

We have already made significant progress within the focal area. We have taken a leading role in developing the Barcelona Declaration on Open Research Information, developed and delivered the course Scientometrics Using Open Data multiple times, used ORI in a variety of projects, contributed to studying and improving the coverage and quality of ORI in open data sources, and pioneered the use of ORI in university rankings through the Leiden Ranking Open Edition. In order to continue this work in a coordinated fashion, we saw a need to develop a strategic plan for our focal area to help guide our efforts. This plan will serve as a framework to guide our activities, to inform decisions about which new projects and collaborations to pursue, and to assess our progress.

Developing the strategic plan: A co-creation process

The strategic plan was developed over a ten-month period in 2025, in which the members of the focal area collaboratively brainstormed where we would like to go and what we would like to achieve in the coming years. The focal area coordinators summarized the group\u2019s input and presented versions of the strategic plan to the focal area members for iterative development. The plan was then further discussed with the other focal area coordinators from Engagement & Inclusion and Evaluation & Culture, and the CWTS Board. Individual conversations were then held with each focal area member in order to discuss how their work and interests align with the plan and to get further feedback.

The strategic plan consists of an overarching vision outlining our long-term ambition, three high-level strategic objectives describing the ideal effects we aim to achieve in the broader research system, and nine operational objectives translating our strategic objectives into actionable steps. We briefly summarize each of these here. More detail can be found in the full version of the plan.

Our vision

The vision of the Information & Openness focal area is:


This vision highlights how ORI can transform the barriers presented by proprietary research information. ORI not only helps make research assessment more transparent and responsible, but it also contributes to developing a more inclusive understanding of what is happening in the research system. By removing barriers and promoting openness, we enable diverse stakeholders such as researchers, institutions, policymakers, and society at large to engage with science in a more democratic and equitable way.

This vision is therefore not isolated but is deeply connected to the broader mission of CWTS and to the other two focal areas. Together, these three focal areas strengthen CWTS\u2019s commitment to improving how science is practiced and governed and how it serves society.

Strategic objectives

The above vision provides the foundation for our strategic objectives, which define the ideal effects we aim to achieve and indicate what the focal area will contribute to in the broader research system. Our three strategic objectives are to:

Operational objectives

Our operational objectives translate these strategic ambitions into concrete, actionable steps. They provide clarity on what needs to be done, ensuring that our vision and strategic goals are not only aspirational but also implementable.

Each operational objective is directly linked to one or more strategic objectives:


More detail about each operational objective, including examples of (planned) projects, services, and courses to achieve these objectives, can be found in the full strategic plan. The objectives are often complementary and interconnected. We also anticipate that there will be some flexibility in the operational objectives and planned results as we put them into practice.

Next steps and collaboration

We plan to re-evaluate the strategic plan annually in order to make necessary modifications to these objectives as the ORI landscape continues to evolve and to ensure continued alignment with our other focal areas. Together, these objectives will help to guide our work in achieving the primary ambition of the Information & Openness focal area: to make open research information the norm, rather than the exception. If you would like to collaborate with us to work towards this vision, please read the strategic plan in more detail and reach out to the coordinators of the focal area. We are looking forward to achieving our vision together.

Header picture by Zizi_zi on Unsplash.
DOI:

","doi":"https://doi.org/10.59350/hzkgh-2er86","funding_references":null,"guid":"https://www.leidenmadtrics.nl/articles/advancing-open-research-information-the-next-three-years-of-the-information-openness-focal-area","id":"1c93ab3e-ebd4-4c95-93ff-6bd2b6be0fe8","image":null,"images":[{"src":"https://www.leidenmadtrics.nl/images/uploads/figure-1_2026-04-21-072830_wdcd.png"},{"src":"https://www.leidenmadtrics.nl/images/uploads/figure-2.png"},{"src":"https://www.leidenmadtrics.nl/images/uploads/figure-3.png"},{"src":"https://www.leidenmadtrics.nl/images/uploads/figure-1_2026-04-21-072830_wdcd.png"},{"src":"https://www.leidenmadtrics.nl/images/uploads/figure-2.png"},{"src":"https://www.leidenmadtrics.nl/images/uploads/figure-3.png"}],"indexed":true,"indexed_at":1777364961,"language":"en","parent_doi":null,"published_at":1777361220,"reference":[],"registered_at":0,"relationships":[],"rid":"x2nee-cxn91","status":"active","summary":"\n The need for open research information\n\nResearch information \u2014 which describes research outputs, activities, and actors \u2014 is essential to understand how science operates, how knowledge is produced, and how research systems evolve. Despite its importance, much of this information remains locked in closed and proprietary systems, creating barriers to its use.","tags":[],"title":"Advancing open research information: The next three years of the Information & Openness focal area","updated_at":1777364794,"url":"https://www.leidenmadtrics.nl/articles/advancing-open-research-information-the-next-three-years-of-the-information-openness-focal-area","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Clarke","given":"Christopher"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"philosophyEthicsAndReligion","community_id":"989c0e4f-140c-47ca-8db1-8490fb2e89d1","created_at":1739378397.350746,"current_feed_url":null,"description":"British Society for the Philosophy of Science","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/989c0e4f-140c-47ca-8db1-8490fb2e89d1/logo","feed_format":"application/atom+xml","feed_url":"https://www.thebsps.org/feed/atom/","filter":"category:49","funding":null,"generator":"WordPress","generator_raw":"WordPress 6.7.2","home_page_url":"https://www.thebsps.org/reviewofbooks/","id":"c168e875-baad-461c-b0af-08e367c182e6","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"thebsps","status":"active","subfield":"1207","subfield_validated":null,"title":"BJPS Review Of Books","updated_at":1777452706.259059,"use_api":true,"use_mastodon":false,"user_id":null},"blog_name":"BJPS Review Of Books","blog_slug":"thebsps","content_html":"
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ROBERT NORTHCOTT
\nSCIENCE FOR A FRAGILE WORLD

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REVIEWED BY
\nChristopher Clarke

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Science for a Fragile World

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Robert Northcott

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Reviewed by
\nChristopher Clarke

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Science for a Fragile World \u25f3
\nRobert Northcott
\nOxford University Press, 2025, \u00a377.00
\nISBN 9780192849083

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Cite as:
\nClarke, C. (2026). \u2018Robert Northcott’s Science for a Fragile World\u2019, BJPS Review of Books,\u00a02026, DOI

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Join the mailing list

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What methods should scientists use to study the world? Robert Northcott\u2019s Science for a Fragile World addresses this general question about the methodology of science by carefully examining some instances of the methods employed in political science, economics, ecology, and epidemiology.

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Northcott\u2019s approach to the methodology of science centres around one crucial distinction\u2014the distinction between stable relations and fragile relations. He draws this distinction in terms of predictability: a stable relation is one where scientists can easily predict how the relation will be instantiated differently from one case to another. Take, for example, the causal effect of an object\u2019s electrical charge on its acceleration (in the presence of another charged object nearby). On the one hand, an object\u2019s acceleration may be difficult to predict, because it may be difficult to measure all the forces acting on the object in question. In this respect, the outcome of interest (acceleration) is subject to a lot of \u2018noise\u2019 generated by factors other than the cause of interest (electrical charge). But, on the other hand, the causal difference that electrical charge makes to an object\u2019s acceleration is easy to predict. That\u2019s because we know Coloumb\u2019s law, which tells us how this causal effect depends upon, and only upon the degree of electric charge, and the distance between the object and any other electrically charged objects in the vicinity. So even though the outcome of interest (acceleration) differs from one case to another, and even though the causal effect of interest (electrostatic force) differs from one case to another as well, this causal effect differs in ways that scientists can predict, given that they know Coloumb\u2019s law. In virtue of this, Northcott counts this causal effect (electrostatic force) as a stable relation.

\n

In contrast, a fragile relation is one where scientists find it difficult to predict how the relation will be instantiated from one case to another. Take lockdown effectiveness, for instance\u2014namely, the causal effect of a national lockdown on the spread of an infectious disease. Lockdown effectiveness probably depends upon a large set of other variables (contextual factors), such as vaccination uptake, prior infections, testing capacity, the timing and duration of the lockdown, trust in government, cultural norms, policing capacity, household size, schooling arrangements, urbanization, the job market, the possibility of remote working, and financial support for social isolation. On the one hand, God knows exactly how the effectiveness of a lockdown will depend upon this long list of contextual factors. In other words, God possesses a theory of lockdown effectiveness that correctly describes a wide variety of cases. (Admittedly, this \u2018wide-scope\u2019 theory is probably rather complex.) On the other hand, scientists do not have warrant for any such wide-scope theory of lockdown effectiveness. And, given our human limitations and given the complexity of disease dynamics, Northcott contends that it\u2019s wishful thinking to suppose that scientists could ever get such warrant. As a result, it is much more difficult for scientists to identify whether a given lockdown was (or will be) effective. In virtue of this, Northcott counts lockdown effectiveness as a fragile relation. Indeed, this illustrates how he defines fragility epistemically rather than metaphysically: fragility denotes scientists\u2019 lack of warrant for any wide-scope theory, and it denotes the corresponding difficulty in predicting how the relation in question will be instantiated in a given case.

\n

Since the fragility of lockdown effectiveness rules out appealing to wide-scope theory, scientists will instead need to become \u2018case workers\u2019, Northcott argues; that is to say, whenever scientists want to identify whether a particular lockdown was (or will be) effective. Being a case worker involves examining the particular lockdown in question in painstaking detail.

\n

Northcott criticizes scientists and philosophers for spending too much time addressing the question of what methods scientists should use to study stable relations, and not enough time addressing the question of what case-worker methods scientists should use to study fragile relations. His book considers all the major research programmes in philosophy of science over the last half century\u2014such as the realism versus instrumentalism debate, the debate on the epistemic import of idealized models, the new mechanist literature, the debate on reflexivity in the social sciences\u2014and it shows how each research programme has ignored or obscured the need for case-worker methods. Northcott then suggests how work in these literatures might be re-orientated to focus on fragile relations and case-worker methods. This, I think, is the major contribution of the book. It\u2019s a manifesto for researchers to invest more time thinking about the proper methodology for studying fragile relations.

\n

If you are not already sold on the importance of fragile relations and case-worker methods, then I wholeheartedly recommend this book. In particular, I\u2019d recommend that you peruse chapter 2 to get the gist of the distinction between stable relations and fragile relations. You might then study chapters 3 and 4 in detail, which is where Northcott illustrates the methodological problem posed by fragile relations (by means of a fascinating case study of informal truces in the trenches of the First World War). These chapters suffice to make these important ideas crystal clear. You can then easily dip into and out of chapters 6 and 7 (which speed through a long list of philosophical research programmes that have obscured the issue of fragile relations and case-worker methods). The same goes for chapter 8\u201310 (which look at three further cases studies drawn from economics, epidemiology, and data science).

\n

But what if you are already sold on the importance of methods for studying fragile relations? Perhaps you are a researcher studying external validity and extrapolation: given the fragility of causal effects, what extra knowledge is needed to extrapolate a causal effect from one context (a randomized controlled trial, for example) to another (Khosrowi 2022)? Or perhaps you are a researcher studying idealized models as tools for inductive inference: given the differences between an idealized model and your target system, what is needed to warrant an induction from what happens in an idealized model to what happens in the real-world target system (Sugden 2022)? For researchers working in the above literatures, the lessons of the book are subtler. Indeed, if you fall into this category, then I\u2019d recommend you focus your attention mostly on chapters 2 and 4.

\n

First, chapter 2 draws a rigorous and clear distinction between stable relations and fragile relations. This gives us philosophers of science a vocabulary with which to describe, as incisively as possible, the exact methodological problem posed by fragile relations. This is a vocabulary of fragile versus stable relations, and of wide-scope versus narrow-scope theories, and of theoretical methods versus case-worker methods.

\n

Second, researchers in the above literatures have inherited a way of thinking about these issues pioneered by Cartwright. But Cartwright\u2019s (1983) early work implicitly assumed that the relations of interest are stable. Indeed, she develops the concept of a causal capacity, which by definition only applies when causal relations are stable, not fragile. In her more recent work on \u2018support factors\u2019, however, it becomes less and less clear whether Cartwright takes herself to be studying fragile relations or stable relations (Cartwright and Hardie 2012). To what extent does Cartwright now believe that social scientists, for example, can, with a lot of effort, get warrant for a wide-scope theory that identifies most of the relevant support factors (contextual factors) upon which a given causal effect depends? It\u2019s not fully clear. Northcott, in contrast, nails his colours to the mast: in most social scientific contexts, fragility abounds, and wide-scope theories are simply not available.

\n

Third, Northcott doesn\u2019t put science up on a pedestal. Some scientific research programmes are fruitful; others are not. And we philosophers of science should be honest about this. For example, social scientists have written thousands of theoretical articles in which they use game theory to offer \u2018how-possibly\u2019 explanations of a huge variety of social phenomena. But this how-possibly theoretical work is often little more than speculation, Northcott points out. This work usually provides no warrant to favour one how-possibly explanation over the countless other explanations that we could have dreamt up for the phenomenon to be explained. If you disagree, then I thoroughly recommend chapters 3 and 4 to you. They constitute a lively, lucid, and incisive challenge to the (in my view exaggerated) epistemic value of much theoretical modelling in the sciences.

\n

Having recommended this book to two different audiences, I want nevertheless to manage your expectations\u2014as well as point towards some directions for future research. This book does not look at the logic behind case-worker methods. Yes, it mentions some examples of case-worker methods, such as interviews or \u2018process tracing\u2019 in political science. And it gives a clear and engaging description of some of the conclusions that process tracers and interviewers have drawn\u2014for example, della Porta\u2019s (1995) ground-breaking work on the dynamics of violent left-wing groups in Germany and Italy. But Northcott\u2019s book does not make explicit the rationale (formal or informal) whereby the evidence della Porta gathers warrants the causal conclusions that she draws. The book shows the need for studying the logic behind case-worker methods, but it does not engage in that study itself.

\n

For readers wanting to engage in that study, I recommend Crasnow\u2019s (2017) work on narratives, Fairfield\u2019s (2022) Bayesian explication of case-worker methods, and work by myself and Rosa Runhardt on the logic behind process tracing (Runhardt 2020; Clarke 2023) and mixed methods research (Clarke 2024; Runhardt unpublished). There\u2019s also the seminal work in Beach and Pedersen (2016) and Brady and Collier (2010) on qualitative case-worker methods in political science. However, if you are not yet convinced for the need for case-worker methods, then I can think of no better place to start than Science for a Fragile World.

\n

\n

\n

Christopher Clarke
\nErasmus University Rotterdam
\nclarke@esphil.eur.nl

\n

\n

\n

References

\n

Beach, D. and Pedersen, R. (2016). Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing, University of Michigan Press.

\n

Brady, H. E., and Collier, D. (2010). Rethinking Social Inquiry: Diverse Tools, Shared Standards, Rowman and Littlefield.

\n

Cartwright, N. (1983). How the Laws of Physics Lie, Oxford University Press.

\n

Cartwright, N. and Hardie, J. (2012). Evidence-Based Policy: A Practical Guide to Doing It Better, Oxford University Press.

\n

Clarke, C. (2023). \u2018Process Tracing: Defining the Undefinable?\u2019, in J. van Bouwel and H. Kincaid (eds), The Oxford Handbook of Philosophy of Political Science, Oxford University Press.

\n

Clarke, C. (2024). \u2018Mixed Methods and Causal Ontology\u2019, in Y. Shan (ed.), Philosophical Foundations of Mixed Methods Research, Routledge, pp. 210\u201339.

\n

Crasnow, S. (2017). \u2018Process Tracing in Political Science: What\u2019s the Story?\u2019, Studies in History and Philosophy of Science A, 62, pp. 6\u201313.

\n

della Porta, D. (1995). Social Movements, Political Violence, and the State, Cambridge University Press.

\n

Fairfield, T. (2022). Social Inquiry and Bayesian Inference: Rethinking Qualitative Research, Cambridge University Press.

\n

Khosrowi, D. (2022). \u2018What\u2019s (Successful) Extrapolation?\u2019, Journal of Economic Methodology, 29, pp. 140\u201352.

\n

Runhardt, R W. (2020). \u2018Concrete Counterfactual Tests for Process-Tracing\u2019, available at <doi.org/10.33774/apsa-2020-0vhbb>.

\n

Runhardt, R W. (unpublished). \u2018Limits to Evidential Pluralism: Multi-method Large-N Qualitative Analysis and the Primacy of Mechanistic Studies\u2019, Synthese, 200, available at <doi.org/10.1007/s11229-022-03650-w>.

\n

Sugden, R. (2022). \u2018Credible Worlds: The Status of Theoretical Models in Economics\u2019, Journal of Economic Methodology, 7, pp. 107\u201336.

\n

\n

\n
\n

Recent Reviews


\n
\"\"

Ellen Clarke, The Units of Life // Reviewed by John Dupr\u00e9

\"\"

Frances Egan, Deflating Mental Representation // Reviewed by Nico Orlandi

\"\"

Jan Baedke, The Organism // Reviewed by Matthew Sims

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\n


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\n

ROBERT NORTHCOTT
\nSCIENCE FOR A FRAGILE WORLD

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\n

REVIEWED BY
\nChristopher Clarke

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\n
\n
\n

Science for a Fragile World

\n

Robert Northcott

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\n

\n

Reviewed by
\nChristopher Clarke

\n

\n

\n

Science for a Fragile World \u25f3
\nRobert Northcott
\nOxford University Press, 2025, \u00a377.00
\nISBN 9780192849083

\n

\n

\n

Cite as:
\nClarke, C. (2026). \u2018Robert Northcott’s Science for a Fragile World\u2019, BJPS Review of Books,\u00a02026, DOI

\n

\n

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\n

\n
Join the mailing list

\n


\n

What methods should scientists use to study the world? Robert Northcott\u2019s Science for a Fragile World addresses this general question about the methodology of science by carefully examining some instances of the methods employed in political science, economics, ecology, and epidemiology.

\n

Northcott\u2019s approach to the methodology of science centres around one crucial distinction\u2014the distinction between stable relations and fragile relations. He draws this distinction in terms of predictability: a stable relation is one where scientists can easily predict how the relation will be instantiated differently from one case to another. Take, for example, the causal effect of an object\u2019s electrical charge on its acceleration (in the presence of another charged object nearby). On the one hand, an object\u2019s acceleration may be difficult to predict, because it may be difficult to measure all the forces acting on the object in question. In this respect, the outcome of interest (acceleration) is subject to a lot of \u2018noise\u2019 generated by factors other than the cause of interest (electrical charge). But, on the other hand, the causal difference that electrical charge makes to an object\u2019s acceleration is easy to predict. That\u2019s because we know Coloumb\u2019s law, which tells us how this causal effect depends upon, and only upon the degree of electric charge, and the distance between the object and any other electrically charged objects in the vicinity. So even though the outcome of interest (acceleration) differs from one case to another, and even though the causal effect of interest (electrostatic force) differs from one case to another as well, this causal effect differs in ways that scientists can predict, given that they know Coloumb\u2019s law. In virtue of this, Northcott counts this causal effect (electrostatic force) as a stable relation.

\n

In contrast, a fragile relation is one where scientists find it difficult to predict how the relation will be instantiated from one case to another. Take lockdown effectiveness, for instance\u2014namely, the causal effect of a national lockdown on the spread of an infectious disease. Lockdown effectiveness probably depends upon a large set of other variables (contextual factors), such as vaccination uptake, prior infections, testing capacity, the timing and duration of the lockdown, trust in government, cultural norms, policing capacity, household size, schooling arrangements, urbanization, the job market, the possibility of remote working, and financial support for social isolation. On the one hand, God knows exactly how the effectiveness of a lockdown will depend upon this long list of contextual factors. In other words, God possesses a theory of lockdown effectiveness that correctly describes a wide variety of cases. (Admittedly, this \u2018wide-scope\u2019 theory is probably rather complex.) On the other hand, scientists do not have warrant for any such wide-scope theory of lockdown effectiveness. And, given our human limitations and given the complexity of disease dynamics, Northcott contends that it\u2019s wishful thinking to suppose that scientists could ever get such warrant. As a result, it is much more difficult for scientists to identify whether a given lockdown was (or will be) effective. In virtue of this, Northcott counts lockdown effectiveness as a fragile relation. Indeed, this illustrates how he defines fragility epistemically rather than metaphysically: fragility denotes scientists\u2019 lack of warrant for any wide-scope theory, and it denotes the corresponding difficulty in predicting how the relation in question will be instantiated in a given case.

\n

Since the fragility of lockdown effectiveness rules out appealing to wide-scope theory, scientists will instead need to become \u2018case workers\u2019, Northcott argues; that is to say, whenever scientists want to identify whether a particular lockdown was (or will be) effective. Being a case worker involves examining the particular lockdown in question in painstaking detail.

\n

Northcott criticizes scientists and philosophers for spending too much time addressing the question of what methods scientists should use to study stable relations, and not enough time addressing the question of what case-worker methods scientists should use to study fragile relations. His book considers all the major research programmes in philosophy of science over the last half century\u2014such as the realism versus instrumentalism debate, the debate on the epistemic import of idealized models, the new mechanist literature, the debate on reflexivity in the social sciences\u2014and it shows how each research programme has ignored or obscured the need for case-worker methods. Northcott then suggests how work in these literatures might be re-orientated to focus on fragile relations and case-worker methods. This, I think, is the major contribution of the book. It\u2019s a manifesto for researchers to invest more time thinking about the proper methodology for studying fragile relations.

\n

If you are not already sold on the importance of fragile relations and case-worker methods, then I wholeheartedly recommend this book. In particular, I\u2019d recommend that you peruse chapter 2 to get the gist of the distinction between stable relations and fragile relations. You might then study chapters 3 and 4 in detail, which is where Northcott illustrates the methodological problem posed by fragile relations (by means of a fascinating case study of informal truces in the trenches of the First World War). These chapters suffice to make these important ideas crystal clear. You can then easily dip into and out of chapters 6 and 7 (which speed through a long list of philosophical research programmes that have obscured the issue of fragile relations and case-worker methods). The same goes for chapter 8\u201310 (which look at three further cases studies drawn from economics, epidemiology, and data science).

\n

But what if you are already sold on the importance of methods for studying fragile relations? Perhaps you are a researcher studying external validity and extrapolation: given the fragility of causal effects, what extra knowledge is needed to extrapolate a causal effect from one context (a randomized controlled trial, for example) to another (Khosrowi 2022)? Or perhaps you are a researcher studying idealized models as tools for inductive inference: given the differences between an idealized model and your target system, what is needed to warrant an induction from what happens in an idealized model to what happens in the real-world target system (Sugden 2022)? For researchers working in the above literatures, the lessons of the book are subtler. Indeed, if you fall into this category, then I\u2019d recommend you focus your attention mostly on chapters 2 and 4.

\n

First, chapter 2 draws a rigorous and clear distinction between stable relations and fragile relations. This gives us philosophers of science a vocabulary with which to describe, as incisively as possible, the exact methodological problem posed by fragile relations. This is a vocabulary of fragile versus stable relations, and of wide-scope versus narrow-scope theories, and of theoretical methods versus case-worker methods.

\n

Second, researchers in the above literatures have inherited a way of thinking about these issues pioneered by Cartwright. But Cartwright\u2019s (1983) early work implicitly assumed that the relations of interest are stable. Indeed, she develops the concept of a causal capacity, which by definition only applies when causal relations are stable, not fragile. In her more recent work on \u2018support factors\u2019, however, it becomes less and less clear whether Cartwright takes herself to be studying fragile relations or stable relations (Cartwright and Hardie 2012). To what extent does Cartwright now believe that social scientists, for example, can, with a lot of effort, get warrant for a wide-scope theory that identifies most of the relevant support factors (contextual factors) upon which a given causal effect depends? It\u2019s not fully clear. Northcott, in contrast, nails his colours to the mast: in most social scientific contexts, fragility abounds, and wide-scope theories are simply not available.

\n

Third, Northcott doesn\u2019t put science up on a pedestal. Some scientific research programmes are fruitful; others are not. And we philosophers of science should be honest about this. For example, social scientists have written thousands of theoretical articles in which they use game theory to offer \u2018how-possibly\u2019 explanations of a huge variety of social phenomena. But this how-possibly theoretical work is often little more than speculation, Northcott points out. This work usually provides no warrant to favour one how-possibly explanation over the countless other explanations that we could have dreamt up for the phenomenon to be explained. If you disagree, then I thoroughly recommend chapters 3 and 4 to you. They constitute a lively, lucid, and incisive challenge to the (in my view exaggerated) epistemic value of much theoretical modelling in the sciences.

\n

Having recommended this book to two different audiences, I want nevertheless to manage your expectations\u2014as well as point towards some directions for future research. This book does not look at the logic behind case-worker methods. Yes, it mentions some examples of case-worker methods, such as interviews or \u2018process tracing\u2019 in political science. And it gives a clear and engaging description of some of the conclusions that process tracers and interviewers have drawn\u2014for example, della Porta\u2019s (1995) ground-breaking work on the dynamics of violent left-wing groups in Germany and Italy. But Northcott\u2019s book does not make explicit the rationale (formal or informal) whereby the evidence della Porta gathers warrants the causal conclusions that she draws. The book shows the need for studying the logic behind case-worker methods, but it does not engage in that study itself.

\n

For readers wanting to engage in that study, I recommend Crasnow\u2019s (2017) work on narratives, Fairfield\u2019s (2022) Bayesian explication of case-worker methods, and work by myself and Rosa Runhardt on the logic behind process tracing (Runhardt 2020; Clarke 2023) and mixed methods research (Clarke 2024; Runhardt unpublished). There\u2019s also the seminal work in Beach and Pedersen (2016) and Brady and Collier (2010) on qualitative case-worker methods in political science. However, if you are not yet convinced for the need for case-worker methods, then I can think of no better place to start than Science for a Fragile World.

\n

\n

\n

Christopher Clarke
\nErasmus University Rotterdam
\nclarke@esphil.eur.nl

\n

\n

\n

References

\n

Beach, D. and Pedersen, R. (2016). Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing, University of Michigan Press.

\n

Brady, H. E., and Collier, D. (2010). Rethinking Social Inquiry: Diverse Tools, Shared Standards, Rowman and Littlefield.

\n

Cartwright, N. (1983). How the Laws of Physics Lie, Oxford University Press.

\n

Cartwright, N. and Hardie, J. (2012). Evidence-Based Policy: A Practical Guide to Doing It Better, Oxford University Press.

\n

Clarke, C. (2023). \u2018Process Tracing: Defining the Undefinable?\u2019, in J. van Bouwel and H. Kincaid (eds), The Oxford Handbook of Philosophy of Political Science, Oxford University Press.

\n

Clarke, C. (2024). \u2018Mixed Methods and Causal Ontology\u2019, in Y. Shan (ed.), Philosophical Foundations of Mixed Methods Research, Routledge, pp. 210\u201339.

\n

Crasnow, S. (2017). \u2018Process Tracing in Political Science: What\u2019s the Story?\u2019, Studies in History and Philosophy of Science A, 62, pp. 6\u201313.

\n

della Porta, D. (1995). Social Movements, Political Violence, and the State, Cambridge University Press.

\n

Fairfield, T. (2022). Social Inquiry and Bayesian Inference: Rethinking Qualitative Research, Cambridge University Press.

\n

Khosrowi, D. (2022). \u2018What\u2019s (Successful) Extrapolation?\u2019, Journal of Economic Methodology, 29, pp. 140\u201352.

\n

Runhardt, R W. (2020). \u2018Concrete Counterfactual Tests for Process-Tracing\u2019, available at <doi.org/10.33774/apsa-2020-0vhbb>.

\n

Runhardt, R W. (unpublished). \u2018Limits to Evidential Pluralism: Multi-method Large-N Qualitative Analysis and the Primacy of Mechanistic Studies\u2019, Synthese, 200, available at <doi.org/10.1007/s11229-022-03650-w>.

\n

Sugden, R. (2022). \u2018Credible Worlds: The Status of Theoretical Models in Economics\u2019, Journal of Economic Methodology, 7, pp. 107\u201336.

\n

\n

\n
\n

Recent Reviews


\n
\"\"

Ellen Clarke, The Units of Life // Reviewed by John Dupr\u00e9

\"\"

Frances Egan, Deflating Mental Representation // Reviewed by Nico Orlandi

\"\"

Jan Baedke, The Organism // Reviewed by Matthew Sims

\n
\n


\n


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\n

\n
\n

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