Community Engagement for Disaster Preparedness in Rural Areas of Mount Merapi, Indonesia
Bibliographic record
Abstract
The rural slopes of Mount Merapi in Yogyakarta Province, Indonesia, existed as vulnerable areas due to recurrent eruptions every few years, necessitating improved strategies to enhance local resilience to volcanic disasters.This study investigates community engagement in fostering disaster preparedness in the Kepuharjo and Umbulharjo, the most vulnerable villages of Merapi slopes in the Cangkringan District of Sleman Regency.Utilizing a qualitative field method, data was collected through on-site observations, document studies, interviews with 14 purposive informants, and FGDs with 21 local voluntary and related stakeholder members.The findings reveal that structural and cognitive social capital mutually reinforce disaster preparedness capacity.The structural dimension evolved with the initiative of resilient village programs, subsequently reinforced by the villagers' engagement in local mitigation actions such as developing volunteer groups, village contingency plans, and diverse communal work for risk prevention.This structural existence was intertwined with the cognitive dimension, referring to the preserved traditional values and beliefs that maintain collective norms and collaboration culture.The finding implies the significance of encouraging structural and cognitive approaches in developing policies to strengthen community-based disaster resilience and, in the theoretical insights, broadening the social capital lens in social studies of disaster.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".