Measuring post-disaster resilience perception in small island: Lessons from 2018 Lombok Island earthquakes in Indonesia
Bibliographic record
Abstract
Disasters expose small islands to heightened risks, which their unique natural conditions and limited economic capabilities further amplify. Malaka Village, located on Lombok Island in West Nusa Tenggara Province, Indonesia, experienced several destructive sequential earthquakes in 2018, providing valuable lessons four years later. A community-based quantitative household survey measures the local community's perceptions of resilience and their correlation with recovery efforts. Local volunteers participated in measuring their community using the local language based on a designed stratified sampling quantitative study employing a five-component framework. This framework assessed access to basic services, regulations and policies in disaster management, prevention and mitigation, emergency preparedness, and recovery readiness. The study finds that Malaka Village is more resilient four years after the 2018 earthquakes, despite a perceived hamlet resilience of 69.9% and a perceived local government resilience of 59.3%. Perceived family or household resilience is higher at 75.6%, which is also concerning, as a quarter of people do not think they are ready to face another disaster. This study recommends ten actions to improve community resilience and identifies the key lessons to enhance community resilience, including access to basic services, understanding disaster risk, and housing recovery capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".