Catalysing gender transformation in research through engaging African science granting councils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it