Strengthening women’s resilience and participation in climate governance in the agrifood sector through public policies: a strategic review of literature
Bibliographic record
Abstract
Women are uniquely vulnerable to climate change but play a critical role in enhancing the climate resilience of the agrifood sector. Based on a rapid review of academic and grey literature, this paper synthesizes the state of knowledge on the level of integration of gender aspects in climate change policies and women’s involvement in policy processes in the Global South. It examines women’s participation in climate change governance, strategies for enhancing this participation, and policy approaches to strengthen women’s resilience while addressing gender inequalities. Findings show that public policies often employ quotas, incentives, and capacity building initiatives to boost women’s participation in governance. However, meaningful engagement in higher-level decision-making remains limited, with quotas sometimes resulting in superficial involvement. Facilitating women’s access to agrifood resources, human capital, and economic opportunities, as well as addressing harmful gender norms, are identified as effective strategies to build resilience. Despite these promising approaches, gaps remain in the implementation and evaluation of policies aimed at enhancing women’s resilience and participation. The paper concludes by recommending outcome-oriented research and robust evaluations of public policy effectiveness in improving women’s climate resilience and governance roles.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.003 | 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".