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Record W4395955857 · doi:10.18280/ijsdp.190427

Community Engagement for Disaster Preparedness in Rural Areas of Mount Merapi, Indonesia

2024· article· en· W4395955857 on OpenAlexvenueno aff
Anang Hermawan, Budi Guntoro, Muhamad Sulhan

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
FundersUniversitas Islam IndonesiaUniversitas Gadjah Mada
KeywordsPreparednessDisaster mitigationMountEmergency managementEnvironmental planningLaharGeographyPolitical scienceEconomic growthEngineeringGeologyVolcanoSeismologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.332
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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