Quantifying professionalism in peer review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.250 | 0.549 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.131 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it