Where women in agri-food systems are at highest climate risk: a methodology for mapping climate–agriculture–gender inequality hotspots
Bibliographic record
Abstract
Climate change poses a greater threat for more exposed and vulnerable countries, communities and social groups. People whose livelihood depends on the agriculture and food sector, especially in low- and middle-income countries (LMICs), face significant risk. In contexts with gendered roles in agri-food systems or where structural constraints to gender equality underlie unequal access to resources and services and constrain women’s agency, local climate hazards and stressors, such as droughts, floods, or shortened crop-growing seasons, tend to negatively affect women more than men and women’s adaptive capacities tend to be more restrained than men’s. Transformation toward just and sustainable agri-food systems in the face of climate change will not only depend on reducing but also on averting aggravated gender inequality in agri-food systems. In this paper, we developed and applied an accessible and versatile methodology to identify and map localities where climate change poses high risk especially for women in agri-food systems because of gendered exposure and vulnerability. We label these localities climate-agriculture-gender inequality hotspots. Applying our methodology to LMICs reveals that the countries at highest risk are majorly situated in Africa and Asia. Applying our methodology for agricultural activity-specific hotspot subnational areas to four focus countries, Mali, Zambia, Pakistan and Bangladesh, for instance, identifies a cluster of districts in Dhaka and Mymensingh divisions in Bangladesh as a hotspot for rice. The relevance and urgency of identifying localities where climate change hits agri-food systems hardest and is likely to negatively affect population groups or sectors that are particularly vulnerable is increasingly acknowledged in the literature and, in the spirit of leaving no one behind, in climate and development policy arenas. Hotspot maps can guide the allocation of scarce resources to most-at-risk populations. The climate-agriculture-gender inequality hotspot maps show where women involved in agri-food systems are at high climate risk while signaling that reducing this risk requires addressing the structural barriers to gender equality.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".