Climate-related disaster opens a window of opportunity for rural poor in northeastern Honduras
Bibliographic record
Abstract
Two distinct views are evident in research on how rural communities in developing countries cope with extreme weather events brought by climate change: (i) that the resource-reliant poor are acutely vulnerable and need external assistance to prepare for such events, and (ii) that climate-related shocks can offer windows of opportunity in which latent local adaptive capacities are triggered, leading to systemic improvement. Results from a longitudinal study in a Tawahka community in Honduras before and after Hurricane Mitch (1994-2002) indicate that residents were highly vulnerable to the hurricane--due in part to previous development assistance--and that the poorest households were the hardest hit. Surprisingly, however, the disaster enabled the poor to initiate an institutional change that led to more equitable land distribution, slowed primary forest conversion, and positioned the community well to cope with comparable flooding occurring 10 y later. The study provides compelling evidence that communities can seize on the window of opportunity created by climate-induced shocks to generate sustained social-ecological improvement, and suggests that future interventions should foster local capacities for endogenous institutional change to enhance community resilience to climate shocks.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".