Disciplinary gender balance, research productivity, and recognition of men and women in academia
Bibliographic record
Abstract
Gender disparities in science have become a salient concern for policy makers and researchers. Previous studies have documented a gender gap in research productivity and recognition in the sciences, and different reasons for this gap have been proposed. In this study, we examine four academic fields with different proportions of men and women in their population. We address the following questions: What is the relationship between the gendered make-up of a field and the productivity and recognition of men and women scientists in that academic field? What is the relationship between the publication patterns of men and women in different academic fields and their productivity and recognition? We find that gendered patterns of productivity and recognition favour men in man-dominated subfields (Mathematical Physics and Software Engineering), while women were more productive and highly cited in one woman-dominated subfield (Nursing), though not in another (Psychology). Nursing, a woman-gendered field, provides an interesting counterpoint to the most usual findings regarding gender disparities in academia. Our findings highlight the need to disaggregate academic fields and to bring to the forefront other disciplines that remain under investigated in analyses of gender gaps to potentially elucidate conflicting findings in the literature.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".