Prospective analyses of sex/gender-related publication decisions in general medical journals: editorial rejection of population-based women’s reproductive physiology
Bibliographic record
Abstract
Objective To assess whether editorial desk rejection at general medical journals (without peer review) of two clinical research manuscripts may relate to author gender or women’s physiology topics. Given evidence for bias related to women in science and medicine, and editorial board attitudes, our hypothesis was that submissions by women authors, on women’s reproductive, non-disease topics received differential editorial assessment. Design A prospective investigation of publications, author gender and topics in general medical journals in two issues following the editorial rejections of two clinical research manuscripts by five major English-language general medical journals. The rejected manuscripts (subsequently published in lower impact journals) described research funded by national granting bodies, in population-based samples, authored by well-published women scientists at accredited institutions and describing innovative women’s reproductive physiology results. Setting Tertiary academic medical centre. Main outcome measures All clinical research published in the two issues following rejection date by each of the five major general medical journals were examined for first/senior author gender. The publication topic was assessed for its gendered population relevance, whether disease or physiology focused, and its funding. Rejection letters assessed editor gender and status. Results Women were underrepresented as original research authors; men were 84% of senior and 69% of first authors. There were no, non-disease focused publications relating to women’s health, although most topics were relevant to both genders. The majority (80%) of rejection letters appeared to be written by junior-ranked women editors. Conclusion Sex/gender accountability is necessary for clinical research-based editorial decisions by major general medical journals. Suggestions to improve gender equity in general medical journal publication: (1) an editorial board sex/gender champion with power to advocate for manuscripts that are well-performed research of relevance to women’s health/physiology; (2) an editorial rejection adjudication committee to review author challenges; and (3) gender parity in double-blind 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.
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 | 0.043 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".