Post-publication critique at top-ranked journals across scientific disciplines: a cross-sectional assessment of policies and practice

Reviewing post-publication peer review

Post-publication peer review (PPPR) is transforming how the life sciences community evaluates published manuscripts and data. Unsurprisingly, however, PPPR is experiencing growing pains, and some elements of the process distinct from standard pre-publication review remain controversial. I discuss the rapid evolution of PPPR, its impact, and the challenges associated with it.

Peer Review at the Crossroads

Peer review has long been regarded as a cornerstone of scholarly communication, ensuring high quality and credibility of published research. Although academic journals trace their origins back three centuries, the procedures for evaluating submissions, particularly peer review, have undergone continuous evolvement. Peer review’s formal institutionalization in the mid-20th century represents a significant, yet natural, phase in this ongoing transformation of scholarly communication. By the early 21st century, there emerged an opinion that the conventional model of peer review faces systematic challenges, including inefficiency, bias, and institutional inertia. The study aims to synthesize the evolution, practices, and outcomes of both conventional and innovative peer review models in scholarly publishing. Through a mixed-methods approach combining interpretative literature review and process modeling (Business Process Model and Notation –BPMN), it identifies four frameworks: pre-publication peer review, registered reports, modular publishing, and the Publish-Review-Curate (PRC) model. While the PRC model, which integrates preprints with post-publication review, demonstrates advantages in transparency and accessibility, no single approach emerges as universally ideal. The choice of model depends on disciplinary context, resource availability, and institutional priorities. The analysis underscores the need for adaptable platforms that enable hybrid workflows, balancing rigor with inclusivity. Future research must address empirical gaps in evaluating these innovations, particularly their long-term impact on equity and epistemic norms.

Scientific production in the era of Large Language Models

Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft manuscripts demonstrate a large increase in paper production, ranging from 23.7-89.3% depending on scientific field and author background, 2) LLM use has reversed the relationship between writing complexity and paper quality, leading to an influx of manuscripts that are linguistically complex but substantively underwhelming, and 3) LLM adopters access and cite more diverse prior work, including books and younger, less-cited documents. These findings highlight a stunning shift in scientific production that will likely require a change in how journals, funding agencies, and tenure committees evaluate scientific works.

Correction of scientific literature: Too little, too late!

The Coronavirus Disease 2019 (COVID-19) pandemic has highlighted the limitations of the current scientific publication system, in which serious post-publication concerns are often addressed too slowly to be effective. In this Perspective, we offer suggestions to improve academia’s willingness and ability to correct errors in an appropriate time frame.

Reformation of science publishing: the Stockholm Declaration

Science relies on integrity and trustworthiness. But scientists under career pressure are lured to purchase fake publications from ‘paper mills’ that use AI-generated data, text and image fabrication. The number of low-quality or fraudulent publications is rising to hundreds of thousands per year, which—if unchecked—will damage the scientific and economic progress of our societies. The result is editor and reviewer fatigue, irreproducible experiments, misguided experiments, disinformation and escalating costs that devour funding from taxpayers intended for research. It is high time to reevaluate current publishing models and outline a global plan to stop this unhealthy development. A conference was therefore organized by the Royal Swedish Academy of Sciences to draft an action plan with specific recommendations, as follows. (i) Academia should resume control of publishing using non-profit publishing models (e.g. diamond open-access). (ii) Adjust incentive systems to merit quality, not quantity, in a reputation economy where the gaming of publication numbers and citation metrics distorts the perception of academic excellence. (iii) Implement mechanisms to prevent and detect fake publications and fraud which are independent of publishers. (iv) Draft and implement legislations, regulations and policies to increase publishing quality and integrity. This is a call to action for universities, academies, science organizations and funders to unite and join this effort.

The strain on scientific publishing

Scientists are increasingly overwhelmed by the volume of articles being published. Total articles indexed in Scopus and Web of Science have grown exponentially in recent years; in 2022 the article total was approximately ~47% higher than in 2016, which has outpaced the limited growth - if any - in the number of practising scientists. Thus, publication workload per scientist (writing, reviewing, editing) has increased dramatically. We define this problem as the strain on scientific publishing. To analyse this strain, we present five data-driven metrics showing publisher growth, processing times, and citation behaviours. We draw these data from web scrapes, requests for data from publishers, and material that is freely available through publisher websites. Our findings are based on millions of papers produced by leading academic publishers. We find specific groups have disproportionately grown in their articles published per year, contributing to this strain. Some publishers enabled this growth by adopting a strategy of hosting special issues, which publish articles with reduced turnaround times. Given pressures on researchers to publish or perish to be competitive for funding applications, this strain was likely amplified by these offers to publish more articles. We also observed widespread year-over-year inflation of journal impact factors coinciding with this strain, which risks confusing quality signals. Such exponential growth cannot be sustained. The metrics we define here should enable this evolving conversation to reach actionable solutions to address the strain on scientific publishing.

A New Paradigm for Scientific Publishing, Peer Review, and Impact Assessment

Scientific publishing and peer review have evolved little in three centuries, while the demands placed on them have grown profoundly. The growing role of artificial intelligence has underscored deep, systemic shortcomings of an aging system that has largely evaded innovation, a system whose origins are appallingly closer to the invention of the printing press than to the internet. We can do better – much better. This article is intended as the beginning of a communal experiment: a living document that critically reviews the modern academic publishing and peer-review system and presents a concrete framework to address what bibliometrics experts¹ have characterized as "the pervasive misapplication of indicators to the evaluation of scientific performance". Building on the Leiden Manifesto, DORA, and a body of scholarship spanning many disciplines and decades, we present a community-governed, non-profit platform organized around three trust-weighted impact factors, for articles, authors, and reviewers, with full algorithmic transparency, an open development log, and structural decoupling of credibility scoring from content moderation and from monetization. We invite the community to discuss, critique, and help shape it.

The review mills, not just (self-)plagiarism in review reports, but a step further

Review mills sum up a new category of reviewer misconduct that flies in the face of reviewer ethics and integrity. A pattern of generic, vague, and repeated affirmations (identical or very similar boilerplate phrasing) is noted in the analysis of 263 review reports, regardless of the scientific content of the papers under review, coupled with coercive citation (perhaps among the main reasons for such behavior), which when combined produce fake reviews. The misconduct associated with review mills is unlike mere plagiarism (self-plagiarism) of reviewer comments. It is important to quantify the problem and to take urgent measures: (a) to identify the review millers; (b) to rectify the published literature; and (c) to determine procedures for journals and publishers on procedures to counter this new type of misconduct.

Does <span style="font-variant:small-caps;">ChatGPT</span> Ignore Article Retractions and Other Reliability Concerns?

ABSTRACT Large language models (LLMs) like ChatGPT seem to be increasingly used for information seeking and analysis, including to support academic literature reviews. To test whether the results might sometimes include retracted research, we identified 217 retracted or otherwise concerning academic studies with high altmetric scores and asked ChatGPT 4o‐mini to evaluate their quality 30 times each. Surprisingly, none of its 6510 reports mentioned that the articles were retracted or had relevant errors, and it gave 190 relatively high scores (world leading, internationally excellent, or close). The 27 articles with the lowest scores were mostly accused of being weak, although the topic (but not the article) was described as controversial in five cases (e.g., about hydroxychloroquine for COVID‐19). In a follow‐up investigation, 61 claims were extracted from retracted articles from the set, and ChatGPT 4o‐mini was asked 10 times whether each was true. It gave a definitive yes or a positive response two‐thirds of the time, including for at least one statement that had been shown to be false over a decade ago. The results therefore emphasise, from an academic knowledge perspective, the importance of verifying information from LLMs when using them for information seeking or analysis.

Does <span style="font-variant:small-caps;">ChatGPT</span> Ignore Article Retractions and Other Reliability Concerns?

ABSTRACT Large language models (LLMs) like ChatGPT seem to be increasingly used for information seeking and analysis, including to support academic literature reviews. To test whether the results might sometimes include retracted research, we identified 217 retracted or otherwise concerning academic studies with high altmetric scores and asked ChatGPT 4o‐mini to evaluate their quality 30 times each. Surprisingly, none of its 6510 reports mentioned that the articles were retracted or had relevant errors, and it gave 190 relatively high scores (world leading, internationally excellent, or close). The 27 articles with the lowest scores were mostly accused of being weak, although the topic (but not the article) was described as controversial in five cases (e.g., about hydroxychloroquine for COVID‐19). In a follow‐up investigation, 61 claims were extracted from retracted articles from the set, and ChatGPT 4o‐mini was asked 10 times whether each was true. It gave a definitive yes or a positive response two‐thirds of the time, including for at least one statement that had been shown to be false over a decade ago. The results therefore emphasise, from an academic knowledge perspective, the importance of verifying information from LLMs when using them for information seeking or analysis.

Making sense of preprints by adding context – The Publish Your Reviews initiative - LSE Impact

Improving scientific publishing is often framed as an issue of openness and speed and less often as one of context. In this post, Ludo Waltman and Jessica Polka make the case for a more contextualised approach to open access publishing and preprinting, and introduce the Publish Your Reviews initiative. Launched today by ASAPbio, the initiative

Fabricated citations: an audit across 2·5 million biomedical papers

Scientific literature depends on the integrity of its references. Each reference implicitly asserts that a verifiable source exists and supports the claims being made. When references point to non-existent studies, readers, reviewers, and policy makers are unable to evaluate the evidence.

Fabricated citations: an audit across 2·5 million biomedical papers

Scientific literature depends on the integrity of its references. Each reference implicitly asserts that a verifiable source exists and supports the claims being made. When references point to non-existent studies, readers, reviewers, and policy makers are unable to evaluate the evidence.

So maybe now we can agree that having an unchecked research integrity militia who unfortunately has the ear of the press and increasingly that of the the publishers, but who fiercely rejects any… | Ioana A. Cristea

So maybe now we can agree that having an unchecked research integrity militia who unfortunately has the ear of the press and increasingly that of the the publishers, but who fiercely rejects any minimal ethics or code of conduct, is a (growing) problem? Also, that 1. problems have to be investigated before deciding they are are legitimate and serious; 2. this investigation is not social media, blogs and the press; 3. this investigation should not be on the front page of journals and Retraction Watch; 4. there are degrees of seriousness and some things can just be corrected or are simply not very consequential (no, it's not a house of bricks where we have to check every brick, that's a dumb analogy), so being absolutely hysterical and overdramatic about any lie, inaccuracy or mistake is purposeful at worse and should be ignored at best and 5. it is not only unnecessary, but harmful, to also go into other, non-academic things the person did or does to complete "investigations" that you (press, sleuth, blogger, etc) do not have the tools and information to do completely and accurately (this fixation would be called harassment in the before times). Others will justly write about what the institutions did or did not do, but what I want to say is that we really should end it with the blank credit we give to any and all allegations that come from the establish research integrity truth fighters.

Screening, sorting, and the feedback cycles that imperil peer review

Scholarly journals rely on peer review to identify the science most worthy of publication. Yet finding willing and qualified reviewers to evaluate manuscripts has become an increasingly challenging task, possibly even threatening the long-term viability of peer review as an institution. What can or should be done to salvage it? Here, we develop mathematical models to reveal the intricate interactions among incentives faced by authors, reviewers, and readers in their endeavors to identify the best science. Two facets are particularly salient. First, peer review partially reveals authors’ private sense of their work’s quality through their decisions of where to send their manuscripts. Second, journals’ reliance on traditionally unpaid and largely unrewarded review labor deprives them of a standard market mechanism—wages—to recruit additional reviewers when review labor is in short supply. We highlight a resulting feedback loop that threatens to overwhelm the peer review system: (1) an increase in submissions overtaxes the pool of suitable peer reviewers; (2) the accuracy of review drops because journals must either solicit assistance from less qualified reviewers or ask current reviewers to do more; (3) as review accuracy drops, submissions further increase as more authors try their luck at venues that might otherwise be a stretch. We illustrate how this cycle is propelled by the increasing emphasis on high-impact publications, the proliferation of journals, and competition among these journals for peer reviews. Finally, we suggest interventions that could slow or even reverse this cycle of peer-review meltdown.