Assessing the Livelihood Vulnerability of Rural Indigenous Households to Climate Changes in Central Nepal, Himalaya
Bibliographic record
Abstract
Climate change and related hazards affect the livelihoods of people and their vulnerability to shocks and stresses. Though research on the linkages between a changing climate and vulnerability has been increasing, only a few studies have examined the caste/ethnicity and gender dimensions of livelihood vulnerability. In this study, we attempt to explore how cultural and gender-related aspects influence livelihood vulnerability in indigenous farming mountain communities of the Nepal Himalaya in the context of climate change. We applied the Livelihood Vulnerability Index (LVI) to estimate household (social group and gender-based) vulnerability in farming communities in the Melamchi River Valley, Nepal. The results identified female-headed families, and those belonging to disadvantaged social groups as more vulnerable and in need of being preferentially targeted by policy measures. Higher exposure to climatic extremes and related hazards, dependency on natural resources, lack of financial assets, and weak social networking were identified as components that determine overall vulnerability. The study also visualizes complex adaptation pathways and analyzes the influence of gender and ethnicity on the capacities of households and communities to adapt to climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".