Women’s representation in national science academies: An unsettling narrative
Bibliographic record
Abstract
Science academies are well placed to contribute towards strengthening of national systems of innovation through advocating for an increased participation of girls and women in science. To successfully do so, academies would need to overcome challenges faced with regard to women’s representation in their own ranks and women’s resultant full participation in the activities of national science academies. We collected baseline data on the representation of women scientists in the membership and governance structures of national science academies that are affiliated with IAP: the Global Network of Science Academies. Women academy members remained far below parity with men, given that women’s membership was typically about 12%. Women members were better represented in the social sciences, humanities and arts but the corresponding shares rarely exceeded 20%. In the natural sciences and engineering, women’s membership remained well below 10%. On average, the largest share of women members (17%) was associated with academies in Latin America and the Caribbean. The average share of women serving on governing bodies was 20%. To change this unsettling narrative, the importance of academies of science annually collecting, analysing and reporting gender-disaggregated data on membership and activities is highlighted as a key recommendation. Several aspects of women’s representation and participation in national science academies are highlighted for further investigation. Significance: Demonstrates under-representation of women in national science academies. Reports on results of the first gender-disaggregated survey on membership and governance of national science academies, globally. Underscores the importance of regular collection, analysis and reporting of gender-disaggregated data in the science sector.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".