Vulnerability to Environmental Risks and Effects on Community Resilience in Mid-West Nepal and South-East Pakistan
Bibliographic record
Abstract
Nepal and Pakistan face a triple challenge of political instability, weak governance and vulnerability to climate change. Communities are highly vulnerable to floods, landslides and droughts. However, the reasons for their vulnerability are complex and differ from location to location. This study has two objectives. First, we analyze and compare the vulnerability of communities to environmental risks in three districts of Nepal with communities in three districts of Pakistan. While we address environmental exposure and sensitivity, the main focus is placed on adaptive capacity including obstacles to adaptation and maladaptation. Second, we explore how the resilience of communities is affected by the combination of environmental risks and weak governance. To identify common and different attributes between and within the two research regions, we apply a comparative conceptual framework to guide the community level case study research conducted in 2011 and 2012 in the Banke, Dang and Rolpa districts of Nepal, and the Badin, Karachi and Thatta districts of Pakistan. We interviewed a total of 288 respondents, including community members and key informants. Our findings suggest that poor governance is a central obstacle to adaptation in both countries but driven by different factors. Examples of maladaptation to climate change risks include provision of rice which undermines the production of traditional crops in Nepal and a water project in Pakistan exposing local communities to floods. The challenge is to improve relations between governance providers and local communities while addressing consequences of environmental risks, including migration and conflict.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".