Implementasi Kebijakan Publik tentang Model Pengurangan Resiko Bencana dengan Pendekatan Pada Masyarakat Dalam Program Desa Tahan Bencana Di Daerah Istimewa Yogyakarta
Bibliographic record
Abstract
The increase in disasters in Indonesia from year to year worries the government and people in disaster-prone areas. For this reason, BNPB (National Disaster Management Agency) launched a community-based program, namely Destana (Disaster Resistant Village) which is implemented by the Regional Disaster Management Agency (BPBD) Special Region of Yogyakarta through public policy. This research focuses on the implementation of the Destana program. The data collection methods were conducted through interviews and narrative question and answer. The results of the research, BPBD DIY has succeeded in forming 243 Destana which are spread across four disctricts and one city of DIY, the community is involved in identifying potential disasters in their area, taking preventive measures, being aware of the impact of disasters and how to deal with them, education, and forming volunteer groups. Conclusion, the Destana program in DIY was implemented by increasing public awareness, the ability to deal with disaster threats and being proactive, participatory and organized as an indication that the implementation of public policy in DIY in reducing disaster risk can be used as a model to reduce the impact of disasters in other 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".