Climate Change and Its Impact on Natural Resources and Rural Livelihoods: Gendered Perspectives from Naryn, Kyrgyzstan
Bibliographic record
Abstract
Climate change poses significant threats to rural communities in Kyrgyzstan, particularly for agriculture, which relies heavily on natural resources. In Naryn Province, rising temperatures and increasing natural hazards amplify vulnerabilities, especially in high mountain areas. Addressing these challenges requires understanding both environmental factors and the perceptions of affected communities, as these shape adaptive responses. This study enhances understanding of climate change impacts on communities in Naryn Province by combining environmental and social assessments through a gendered lens, with a particular focus on women. Environmental data, including air temperature, precipitation, river discharge, and satellite-derived vegetation indices, were analyzed to evaluate changes in vegetation and water resources. Social data were collected through interviews with 298 respondents (148 women and 150 men) across villages along the Naryn River, with chi-square analysis used to examine gender-specific perceptions and impacts on livelihoods. The results indicated a noticeable rise in temperatures and a slight decline in precipitation over recent decades, affecting vegetation and grazing areas near settlements. While respondents of both genders reported similar observations, differences emerged in how changes affect their roles and activities, with localized variations linked to household and agricultural responsibilities. The findings highlight the need for inclusive adaptation strategies that address diverse experiences and priorities, providing a foundation for equitable and effective climate resilience measures.
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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 | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".