Assessment of Postpublication Critique Policies and Practices at Top-Ranked Journals in 22 Scientific Disciplines | VIDEO
Bibliographic record
Abstract
Objective To describe how top-ranked journals across 22 scientific disciplines handle postpublication critique such as letters, commentaries, and online comments.1-3 Design Cross-sectional assessment of policies and practices related to postpublication critique at 15 journals (top-ranked by impact factor) operating in each of 22 scientific disciplines (defined by Clarivate Essential Science Indicators) assigned to 5 high-level scientific domains (defined by the authors; 330 journals). Policy information was extracted from journal websites in November 2019. For each journal offering postpublication critique, a random sample of 10 research articles published in 2018 (2066 articles) was examined to see if they were linked to postpublication critique on the article’s webpage (1 journal only published 6 articles in 2018). Features of all linked postpublication critiques and associated author replies were recorded. Results Overall, 207 of 330 journals (63%) offered postpublication critique such as letters (118), commentaries (85), or web comments (41) but often imposed limits on length (median, 1000; IQR, 500-1200 words) and time to-submit (median, 12; IQR, 4-26 weeks). The most restrictive limits were 175 words and 2 weeks; the least restrictive policies had no limits. Seventy-four journal policies implied independent external peer review of postpublication critique. Of a random sample of 2066 research articles published by journals offering postpublication critique, 39 (1.9%; 95% CI, 1.4%-2.6%) were linked to at least 1 postpublication critique (there were 58 postpublication critiques in total). Of these target articles, 34 were from the health and life sciences and 5 were from multidisciplinary journals. Examination of all 58 postpublication critiques found that they addressed issues related to design (19), implementation (3), analysis (19), reporting (10), interpretation (45), and ethics (1); 29 were paywalled; 45 had conflict of interest statements, 15 of which declared a potential conflict; 44 received an author reply, of which 41 asserted that the authors’ conclusions were unchanged. Fifty-one did not include any novel statistical analyses of original or new data, though only 3 target articles stated that data were available. The health and life sciences and multidisciplinary journals offered and published more postpublication critiques relative to other domains (Table 23). Clinical medicine in particular stood out, with the highest prevalence of postpublication critique (13% of 150 articles) and all 15 journals allowing postpublication critique. However, these journals also imposed the strictest limits on length (median, 400; IQR, 400-550 words) and time to submit (median, 4; IQR, 4-6 weeks). https://assets.underline.io/uploads/markdown_image/1/image/b35d220db57f1034bd195c12ec3bb6cf.png Conclusions Top-ranked academic journals across scientific disciplines often pose barriers to the cultivation, documentation, and dissemination of postpublication critique. Publication of postpublication critique was rare in most disciplines. Published postpublication critique may have little effect on authors’ conclusions. References 1. Bastian H. A stronger post-publication culture is needed for better science. PLoS Medicine. 2014;11:e1001772. doi:10.1371/journal.pmed.1001772 2. Altman DG. Poor-quality medical research: what can journals do? JAMA. 2002;287:2765-2767. doi:10.1001/ jama.287.21.2765 3. Winker MA. Letters and comments published in response to research: whither postpublication peer review? Abstract presented at: International Congress on Peer Review and Scientific Publication; September 9, 2013; Chicago, Illinois. https://peerreviewcongress.org/2013-abstracts/ Conflict of Interest Disclosures Tom E. Hardwicke receives funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 841188. Robert T. Thibault is supported by a general support grant awarded to METRICS from the Laura and John Arnold Foundation and a postdoctoral fellowship from the Fonds de recherche du Québec–Santé. Theiss Bendixen thanks the Aarhus University Research Foundation for support. Jessica E. Kosie received funding from NSF SBE Postdoctoral Research Fellowship 2004983 and NIH F32 National Research Service Award HD103439. Loukia Tzavella was supported by ESRC postdoctoral fellowship ES/V011030/1. No other conflicts were reported. Funding/Support The Meta-Research Innovation Center at Stanford (METRICS) is supported by a grant from the Laura and John Arnold Foundation. The Meta-Research Innovation Center Berlin (METRIC-B) is supported by a grant from the Einstein Foundation and Stiftung Charité. Role of the Funder/Sponsor The funders had no role in this research. https://assets.underline.io/uploads/markdown_image/1/image/d310793ae5bde69eb821766e842f3b35.png
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.251 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".