Diversity begets diversity: A global perspective on gender equality in scientific society leadership
Bibliographic record
Abstract
Research shows that gender inequality is still a major issue in academic science, yet academic societies may serve as underappreciated and effective avenues for promoting female leadership. That is, society membership is often self-selective, and board positions are elected (with a high turnover compared to institutions)-these characteristics, among others, may thus create an environment conducive to gender equality. We therefore investigate this potential using an information-theoretic approach to quantify gender equality (male:female ratios) in zoology society boards around the world. We compare alternative models to analyze how society characteristics might predict or correlate with the proportion of female leaders, and find that a cultural model, including society age, size of board and whether or not a society had an outward commitment or statement of equality, was the most informative predictor for the gender ratio of society boards and leadership positions. This model was more informative than alternatives that considered, for instance, geographic location, discipline of study or taxonomic focus. While women were more highly represented in society leadership than in institutional academic leadership, this representation was still far short of equal (~30%): we thus also provide a checklist and recommendations for societies to contribute to global gender equality in science.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".