Catalysing gender transformation in research through engaging African science granting councils
Bibliographic record
Abstract
Science investments should benefit everyone; however, research still predominantly lacks gender integration, resulting in incomplete findings and inequitable outcomes. Moreover, despite some progress, gender disparities persist in the research workforce. Research funders, including science granting councils, are pivotal in driving gender transformation through shaping knowledge production and research infrastructure. We report on key findings from the Science Granting Councils Initiative (SGCI) in Sub-Saharan Africa (SSA) Gender Equality and Inclusivity (GEI) Project – a multi-year participatory intervention aimed at strengthening the capacities of councils to integrate GEI across their functions. Participating councils were located in 13 African countries, and their actions spanned four domains: building organisational GEI infrastructure; reshaping norms, practices, and power relations that perpetuate gender inequality; implementing targeted measures to address women’s unequal access to resources and research opportunities; and promoting collective ownership of efforts to advance GEI in the research and innovation ecosystem.
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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 | 0.128 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".