Sex, Gender, and Diversity Analysis in Research Policies of Major Public Granting Agencies: A Global Review
Bibliographic record
Abstract
National research agencies are responsible for promoting excellent research that benefits all of society (1). Integrating sex, gender, and diversity analysis (SG&DA) into the design of research, where relevant, can improve research methodology, enhance excellence in science, and make research more responsive to social needs (2). National funding agencies—encouraged by scientists and social movements—have thus begun to implement policies to integrate sex, gender, and, more recently, diversity analysis into the grant proposal process, where these factors have been shown to play a role. We develop a five-part analytical framework for implementing and evaluating SG&DA policies, and use it to evaluate the quality of SG&DA policies for 22 major national funding agencies across six continents. By collecting emerging global practices for policy implementation, we seek to improve understanding of these policies and practices in efforts to enhance international collaborations and research excellence.
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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 | 0.041 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".