CGIAR Gender Equality in Food Systems Research Platform Proposal Resubmission
Bibliographic record
Abstract
Impactful gender research within CGIAR is a precondition to achieving its mission of enabling people, especially women, to better nourish their families, while improving food system productivity and resilience. In the face of climate change and demographic shifts, such research has become more important than ever before, and is now essential for shared economic growth, manage natural resources and improve the lives of women and men. The CGIAR Generating Evidence and New Directions for Equitable Results (GENDER) Platform that we propose will catalyse targeted research on gender equality in agriculture and effectively collaborate with decision-makers to achieve a new normal: a world in which gender equality drives a transformation towards equitable, sustainable, productive and climate-resilient food systems.
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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.463 | 0.250 |
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".