Quantifying professionalism in peer review
Bibliographic record
Abstract
BACKGROUND: The process of peer-review in academia has attracted criticism surrounding issues of bias, fairness, and professionalism; however, frequency of occurrence of such comments is unknown. METHODS: We evaluated 1491 sets of reviewer comments from the fields of "Ecology and Evolution" and "Behavioural Medicine," of which 920 were retrieved from the online review repository Publons and 571 were obtained from six early career investigators. Comment sets were coded for the occurrence of "unprofessional comments" and "incomplete, inaccurate or unsubstantiated critiques" using an a-prior rubric based on our published research. Results are presented as absolute numbers and percentages. RESULTS: Overall, 12% (179) of comment sets included at least one unprofessional comment towards the author or their work, and 41% (611) contained incomplete, inaccurate of unsubstantiated critiques (IIUC). CONCLUSIONS: The large number of unprofessional comments, and IIUCs observed could heighten psychological distress among investigators, particularly those at an early stage in their career. We suggest that development and adherence to a universally agreed upon reviewer code of conduct is necessary to improve the quality and professional experience of peer review.
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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 | 0.513 | 0.855 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".