Challenges and Development of Disaster Mitigation Policies in North Luwu Regency: Strengthening Post-Disaster Resilience
Bibliographic record
Abstract
The urgency of taking cases in North Luwu lies in the high risk of flood and landslide disasters that threaten the community's safety and welfare.This research aims to identify the main challenges in disaster mitigation policies in North Luwu Regency and explore strategic approaches that local governments can adopt to strengthen post-disaster resilience in North Luwu Regency.This research method adopts a qualitative approach by collecting data through interviews, documentation, and field observations involving informants, as well as collecting relevant official documents to understand disaster mitigation in North Luwu Regency.The data obtained was analyzed using Nvivo 12 Plus with a coding approach and validated through triangulation and member checking to ensure the consistency and accuracy of the findings.The findings of this study show that the significant challenges faced include a lack of coordination between agencies, which causes fragmentation in disaster management, overlapping programs, which result in waste of resources, inadequate funding, and a lack of skilled human resources in disaster management.This is also exacerbated by distrust towards the government, which affects public participation.Recommendations for overcoming this challenge include building inter-agency coordination through forums involving many parties, increasing transparency and accountability through integrated information systems, encouraging active community participation in recovery planning, and developing human resource capacity through disaster management training and building public trust in the government, through effective communication and open dialogue.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".