Gender Imbalance in Authorship of Veterinary Literature: 1995 versus 2015
Bibliographic record
Abstract
Despite increasing representation of women in veterinary medicine, gender differences persist in pay and attainment of senior and leadership positions. In academia, scholarly publication is a measure of productivity and is emphasized in the promotion process. This study aimed to analyze gender differences in the authorship of veterinary research articles to understand factors that could influence women's advancement and standing in academic medicine. We hypothesized that the proportion of women authors would increase between 1995 and 2015 and be similar to employment rates of women in academia, and that gender differences would exist in authorship by species, veterinary specialty area, and role (junior versus senior author). We examined 2,086 articles published in eight prominent veterinary journals in 1995 and 2015, determined the gender of first authors, corresponding authors, and senior authors, and collected article information including study design, species, and veterinary specialty area. The proportion of women as first and corresponding author increased significantly between 1995 and 2015, and in both years studied, women authored a larger percentage of articles than the reported percentage of women working in academia. In 2015, women were first authors of 60.0% (95% CI 56.9-63.0) of articles but accounted for only 38.3% of senior authors (95% CI 33.4-43.3). Female first authors were concentrated in articles pertaining to small animal, equine, and internal medicine disciplines and under-represented among articles pertaining to livestock or surgical specialties. The gender gap in the authorship of veterinary clinical research articles has improved dramatically over the past 20 years, although gender disparities persist.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".