Women’s Adaptation Strategies for Ensuring Food Security to Response Climate Change: Good Practice from Rural Swamp in Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
This research examines rural women's strategies and adaptive capacity in the Rawa Lebak region in responding to climate change and ensuring family food security.As primary household food providers, rural women face growing challenges due to climate change, directly impacting food production and availability.Climate change is a global concern addressed in SDG 13 (climate action), while food security is a priority under SDG 2. A mixed methods approach is used in this research.The quantitative analysis evaluates rural women's adaptive capacity by assessing economic resources, human capital, production and marketing infrastructure, institutional support, social capital, and natural resources.The qualitative component explores their strategies and activities in maintaining family food security amid climate shifts.Findings reveal clear indicators of climate change in Muara Menang village, including seasonal shifts, prolonged droughts, floods, and land fires.However, women's understanding of climate change remains limited, often perceived only as seasonal variations.These environmental disruptions contribute to crop failures, exacerbating food insecurity and destabilizing household food supplies.Given their responsibility for food provision, rural women must adapt by developing innovative strategies to sustain food availability.Their resilience and adaptive measures play a crucial role in mitigating the adverse effects of climate change on family food security.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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