Women’s Adaptation Strategies for Ensuring Food Security to Response Climate Change: Good Practice from Rural Swamp in Indonesia
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".