Reviewer #1 (Public Review): Gender differences in submission behavior exacerbate publication disparities in elite journals
Bibliographic record
Abstract
Women are particularly underrepresented in journals of the highest scientific impact, with substantial consequences for their careers. While a large body of research has focused on the outcome and the process of peer review, fewer articles have explicitly focused on gendered submission behavior and the explanations for these differences. In our study of nearly five thousand active authors, we find that women are less likely to report having submitted papers and, when they have, to submit fewer manuscripts, on average, than men. Women were more likely to indicate that they did not submit their papers (in general and their subsequently most cited papers) to Science, Nature, or PNAS because they were advised not to. In the aggregate, no statistically significant difference was observed between men and women in how they rated the quality of their work. Nevertheless, regardless of discipline, women were more likely than men to indicate that their “work was not ground-breaking or sufficiently novel” as a rationale for not submitting to one of the listed prestigious journals. Men were more likely than women to indicate that the “work would fit better in a more specialized journal.” We discuss the implications of these findings and interventions that can serve to mitigate the disparities caused by gendered differences in submission behavior.Publishing in high-impact scholarly journals has a significant effect on researchers’ careers. Our findings identify factors that affect submission to Science, Nature, and the Proceedings of the National Academy of Sciences of the United States of America (PNAS) and explore whether there is a relationship between gender and desk rejections or submission rates. We found no relationship between gender and reported desk rejection and a relationship between gender and reported submissions. Women were more likely than men to indicate that their “work was not ground-breaking or sufficiently novel” for the listed prestigious journals and that they were advised against submitting to these venues. Men were more likely to indicate that the “work would fit better in a more specialized journal.”
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 | 0.060 | 0.339 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".