On the relationship between gender disparities in scholarly communication and country-level development indicators
Bibliographic record
Abstract
Gender disparities in science remain, despite decades of policies aimed at achieving gender parity. Yet, little is known about the macro-level factors affecting such disparities. This paper examines the degree to which country-level human development indicators (HDI) and gender inequality indicators (GII) gathered by the United Nations Development Report can reveal systemic gender inequalities in scholarship. Countries ‘low’ in HDI and GII had the lowest contribution of female participation in science and highest degree of international collaboration. Research from highly developed countries was more cited, although gender disparities remained. For HDI, gross national income was a strong predictor of scientific output and impact (and, to a lesser degree, collaboration). The rate of women in the labor force was the strongest predictive variable in GII, explaining differences in output, collaboration, and impact. However, predictive variables differed by HDI/GII quartile, suggesting that monolithic policies may not be appropriate for addressing gender disparities 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".