Reconstructing Land Fire Mitigation Policies in Solok Regency, Indonesia: A Disaster Management Continuum Approach
Bibliographic record
Abstract
Mitigation constitutes a comprehensive endeavor to eliminate or diminish the risks associated with disaster hazards that impact vulnerable populations.The prevention and management of land fire disasters ought to be conducted by governmental agencies in alignment with their designated responsibilities and functions; however, these initiatives have not been executed satisfactorily or optimally and have demonstrated ineffectiveness in both the prevention and management of disaster mitigation strategies, particularly concerning land fire disasters.In light of this, this study aims to reconstruct the progression of land fire disaster mitigation policies utilizing the disaster management continuum model within the context of Solok Regency, Indonesia.The disaster management continuum model is extensively acknowledged as the most efficacious framework due to its representation of various stages, aiding implementation.The methodology employed in this research is qualitative, as it is anticipated to facilitate a reconstruction of land fire disaster mitigation policies through the lens of the disaster management continuum approach.The scope of this study is confined to the aspects of land fire disaster mitigation policies that emphasize participatory engagement.The research findings reveal that the practical application of the disaster mitigation model utilizing the disaster management continuum method is substantiated by comprehensive and effective implementation.The reconstruction of the land fire disaster mitigation model can serve as an educational instrument for the community regarding land fires, thereby potentially mitigating the risk of substantial losses.This is essential to thoroughly comprehend local conditions, geological characteristics, climatic factors, and distinctive socio-economic variables in specific regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".