Characterizing Global Gender Representation in Veterinary Executive Leadership
Bibliographic record
Abstract
Veterinary medicine is an increasingly feminized field, with growing numbers of veterinary students and professionals identifying as women. Increased representation of women in senior veterinary education leadership has not yet been examined across all global regions. To address this question, we compiled a comprehensive list of veterinary academic executives from veterinary educational institutions listed by the World Veterinary Association, the American Veterinary Medical Association, and the World Organisation for Animal Health. In total, data from 720 veterinary schools in 118 countries were obtained via an online search of each school's webpage to retrieve information on executive-level leaders and their gender representation. Out of 2,263 executive leaders included, 784 (34.6%) were inferred to be women. Of 733 top executives-deans or their equivalents-187 (25.5%) were inferred to be women. At the national level, the proportion of women in executive teams was positively correlated with Gross Domestic Product, Gender Development Index, and negatively correlated with Gender Inequality Index. This is the first study to demonstrate inequity in the gender composition of veterinary educational leadership across the majority of veterinary schools worldwide, and regional trends thereof. It also identifies potential socioeconomic issues closely connected to gender equity in these spaces. To monitor progress toward gender equity within the profession, future work is needed to assess gender representation over different phases of veterinary career tracks, including in student populations. Analysis of gendered trends over time will also help to establish trends and evaluate progress in gender equity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".