Governing agrifood systems for climate resilience and gender inclusivity: A strategic review of the evidence
Bibliographic record
Abstract
Women’s limited voice in governance and decision-making impedes inclusive climate resilience. This scoping review identifies barriers that hamper women’s participation in AFS decision-making and assesses interventions that amplify their voice and agency. Drawing on two analytical frameworks—the women’s empowerment in AFS governance framework (Ragasa et al., 2022) and the Reach-Benefit-Empower-Transform (RBET) framework (Quisumbing et al., 2023)—we synthesize evidence from 47 studies in the Global South. Barriers are found in two domains: access to climate-relevant agricultural innovations and exclusion from local governance processes. Best practices include gender-responsive extension, social innovations such as self-help groups and digital tools, and organizational strategies including gender budgeting and men’s engagement. We conclude that advancing women's leadership in AFS governance requires multi-level interventions that address structural, sociocultural, and informational inequities.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".