Assessing the Livelihood Vulnerability to Impact of Climate Change in Western Bhutan
Bibliographic record
Abstract
Climate change possesses vagaries threats to the subsequent livelihood of the people in Bhutan and it is crucial to enhance adaptive capacity. Therefore, building resiliency requires information on vulnerability of the system of interest. Therefore, this study assessed smallholder farmer’s vulnerability to impacts of climate change and variability in western parts (Punakha, Wangdue, Gasa) of Bhutan. A survey was conducted from 392 randomly selected households based on major components of sociodemographic profiles, livelihood strategies, health, social network, food, water and natural disaster and climate variability. Data was analyzed using Livelihood Vulnerability Index approach (LVI) and IPCC framework approach (LVI-IPCC). The result indicated that the LVI (range 0.39 to 0.43) and LVI-IPCC (range -0.018 to 0.030) varied across the districts. Punakha district (0.43) was most vulnerable by the LVI approach, whereas Gasa district (0.03) revealed as most vulnerable using LVI-IPCC approach. The rate of vulnerability in a district varied according to their degree of exposure and adaptive capacity to the impacts of climate change among smallholder farmers. Higher exposure to climatic extremes, dependency on natural resources and weak social networking were recognized as components that determine vulnerability. The results are expected to serve an indication to design appropriate intervention to cope with climate change impacts and increase resiliency for sustainable livelihood.
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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".