Gender Distribution Associated With the Journal <i>Wilderness & Environmental Medicine</i>
Bibliographic record
Abstract
INTRODUCTION: Publication and peer review are fundamental to career advancement in science and academic medicine. Studies demonstrate that women are underrepresented in science publishing. We evaluated the gender distribution of contributors to Wilderness & Environmental Medicine (WEM) from 2010 through 2019. METHODS: We extracted author data from ScienceDirect, reviewer data from the WEM Editorial Manager database, and editorial board data from journal records. Gender (female and male) was classified using automated probability-based assessment with Genderize.io software. RESULTS: A total of 2297 unique authors were published over the 10-y span, generating 3613 authorships, of which gender was classified for 96% (n=3480). Women represented 26% (n=572) of all authors, which breaks down to 22% of all, 19% of first, 28% of second, and 18% of last authorships. Women represented 20% of peer reviewers (508/2517), 20% of reviewers-in-training (19/72), and 16% of editorial board members (7/45). The proportion of female authors, first authors, and reviewers increased over time. Women received fewer invitations per reviewer than men (mean 2.1 [95% CI 2.0-2.3] vs 2.4 [95% CI 2.3-2.5]; P=0.004), accepted reviews at similar rates (mean 73 vs 71%; P=0.214), and returned reviews 1.4 d later (mean 10.4 [CI 9.5-11.3] vs 9.0 d [95% CI 8.5-9.6]; P=0.005). CONCLUSIONS: While female representation increased over the study period, women comprise a minority of WEM authors, peer reviewers, and editorial board members. Gender equity could be improved by identifying and eliminating barriers to participation, addressing any potential bias in review processes, implementing strategies to increase female-authored submissions, and increasing mentorship and training.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".