Investigation of potential gender bias in the peer review system at <i>Reproduction</i>
Bibliographic record
Abstract
Abstract This study examined whether publication outcome was affected by the gender of author, handling associate editor (AE), or reviewer, and whether there was gender bias in reviewer selection, in the journal Reproduction. Analyses were carried out on 4289 original research manuscripts submitted to the journal between 2007 and 2019. Both female and male AEs appointed more male reviewers than female reviewers, but female AEs were significantly more likely to appoint female reviewers than male AEs were (p < 0.001). When examining the gender of either first or last author manuscripts, those with female authors that were reviewed by female reviewers received better scores than those with male authors that were reviewed by female reviewers (p < 0.05): where the reviewer was male, no such effect was observed. Acceptance rates of manuscripts were similar for both female and male authors, whether first or last, regardless of AE gender. Overall, there was no significant correlation between gender of first or last author, or of AE, on the likelihood of acceptance of a research paper. These data suggest no bias against female authors during the peer review process in this reproductive biology journal.
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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 | 0.137 | 0.393 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".