Strengthening disaster mitigation of mount Sinabung through the integration of local wisdom and social resilience: a participatory study in Karo Regency
Bibliographic record
Abstract
This study aims to develop a disaster mitigation model for the eruption of Mount Sinabung based on the integration of local wisdom from the Karo community and social resilience theory. The recurring eruptions since 2010 have resulted in complex socio-ecological impacts, yet existing mitigation approaches have tended to be technocratic and lacked contextual relevance. This research employs a descriptive qualitative approach within a constructivist paradigm, relying on in-depth interviews, observation, documentation, and Focus Group Discussions (FGDs) as data collection techniques. Data analysis is conducted using the Miles, Huberman, and Saldana model, encompassing data reduction, presentation, and conclusion drawing. The main finding of this study is the formulation of the RiRi Model, which comprises four components: Respect (recognition of local knowledge), Initiation (active community participation), Runggun (optimization of traditional social networks), and Integration (collaboration between local and modern approaches). This model offers a more inclusive mitigation strategy, rooted in the cultural values of the Karo community, and capable of holistically strengthening community resilience. The study also reveals that local wisdom practices such as aron, runggu, and the use of jambur hold strategic potential as community-based disaster mitigation instruments. By synergizing social, cultural, and technical dimensions, the RiRi Model is expected to serve as an alternative framework for national disaster mitigation policy that is more adaptive, sustainable, and responsive to local contexts.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".