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Record W7115719532 · doi:10.48448/sg1t-qy03

[V] Policies on Artificial Intelligence Among Academic Publishers

2025· other· W7115719532 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityAccreditationAuditPublic policyPublishingDescriptive statistics

Abstract

fetched live from OpenAlex

Jeremy Y. Ng,<sup>1,2,3</sup> Daivat Bhavsar,<sup>4</sup> Laura Duffy,<sup>4</sup> Hamin Jo,<sup>4</sup> Cynthia Lokker,<sup>4</sup> R. Brian Haynes,<sup>4,5</sup> Alfonso Iorio,<sup>4,5</sup> Ana Marušić<sup>6</sup> <h4>Objective </h4> This study examined the policies implemented by academic publishers regarding authors’ use of generative artificial intelligence (GenAI) tools, focusing on their regulation, disclosure requirements, and role in ensuring the integrity of scientific publications. By analyzing the prevalence and content of these policies, this study aimed to provide insight into the current landscape and inform future policy development in the rapidly evolving field of artificial intelligence (AI)–assisted research and publication. <h4>Design </h4> A cross-sectional audit was conducted on the publicly available policies of 163 academic publishers listed as members of the International Association of Scientific, Technical, and Medical Publishers. Policies were collected and analyzed between September 1 and December 31, 2023. Publishers without publicly accessible policies specific to GenAI use by authors were excluded. Data extraction and analysis were conducted independently in duplicate, with a third reviewer resolving discrepancies. The key policy components analyzed included authorship accreditation, disclosure requirements, and permissions for tasks such as research methods, content generation, image creation, and proofreading. Descriptive statistics were used to summarize the findings. Our protocol was registered.<sup>1</sup> <h4>Results </h4> Of 163 academic publishers, 56 (34.4%) had publicly available policies guiding GenAI use by authors. None permitted authorship accreditation for AI tools, citing accountability concerns and alignment with ethical guidelines. Nearly all publishers with policies (49 of 56 [87.5%]) mandated disclosure of GenAI use, primarily in the Methods or Acknowledgments section. However, disclosure practices varied, with some publishers providing standardized templates while others left requirements vague. Four publishers completely prohibited GenAI use in manuscript preparation, while others allowed their use for specific tasks. Most (33 of 56 [58.9%]) publishers permitted GenAI for drafting nonmethodological sections (eg, Introductions), while 18 (32.1%) permitted their use in research methods, such as data analysis and organization. Few publishers addressed GenAI use in image generation (14 of 163 [8.6%]) or proofreading (15 of 163 [9.2%]). Only 1 publisher (0.6%) allowed citation of AI as primary sources, while 19 (11.6%) explicitly prohibited such citations. Our study has been published.<sup>2</sup> <h4>Conclusions </h4> This audit highlights the inconsistent development of GenAI policies among academic publishers, with large variability in scope and clarity. While the prohibition of AI authorship and the emphasis on mandatory disclosure are consistent themes, inconsistencies in regulating specific tasks suggest a need for standardized and comprehensive policies. As AI technology and its applications in research evolve, publishers must adapt to safeguard scientific integrity. Given that the AI landscape is fast moving, future research includes updating this audit and comparing and contrasting current policies with those found in this study. Future research should also assess how policies are implemented and enforced by examining samples of published articles, as well as explore how these policies affect editors and reviewers, taking into account potential risks, such as privacy breaches and bias. <h4>References</h4> 1. Bhavsar D, Lokker C, Haynes RB, Iorio A, Marusic A, Ng JY. Academic publisher artificial intelligence chatbot policies for authors: a cross-sectional audit. OSF Registries. Accessed July 11, 2025. doi:10.17605/OSF.IO/937ES <span lang="fr-FR">2. Bhavsar D, Duffy L, Jo H, et al. Policies on artificial intelligence chatbots among academic publishers: a cross-sectional audit. </span><i>Res Integr Peer Rev</i><span lang="fr-FR">. 2025;10(1):1. </span>doi:10.1186/s41073-025-00158-y <sup>1</sup>Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany, jeremyyng.phd@gmail.com; <sup>2</sup>Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany; <sup>3</sup>Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada; <sup>4</sup>Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; <sup>5</sup>Department of Medicine, McMaster University, Hamilton, Ontario, Canada; <sup>6</sup>Department of Research in Biomedicine and Health and Center for Evidence-Based Medicine, University of Split School of Medicine, Split, Croatia. <h4>Conflicts of Interest Disclosures </h4> The authors declare no conflicts of interest. Ana Marušić is a member of the Peer Review Congress Advisory Board but was not involved in the review or decision for this abstract.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0130.021
Science and technology studies0.0020.030
Scholarly communication0.0050.004
Open science0.0120.003
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0130.035

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.352
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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