Policy Design for Strengthening Disaster Risk Reduction Based on Sendai Framework for Action in West Sumatera Province, Indonesia
Bibliographic record
Abstract
This research aims to discuss the design of policies to strengthen disaster risk reduction based on the Sendai Framework for Action.This research is a relatively new study because it formulates a policy design based on the Sendai Framework to improve the quality of disaster risk governance.This research was conducted in West Sumatra Province using evaluative qualitative methods, and the NVIVO 12 Plus application was used to test and analyze data and design research results.The findings of this research indicate that there are problems with institutional regulations, accountability, and human resource capacity in reducing the risk of disasters in West Sumatra Province.Based on the research findings, the results showed that stakeholders declared strengthening the Sendai Framework dimensions acceptable.To enhance the Sendai Framework dimensions, designing and implementing policies and programs to strengthen policies and regulations, community empowerment, increasing human resource capacity, and budgeting related to disaster risk reduction in West Sumatra Province is necessary.This research implies that conceptually and practically, it is a breakthrough in providing policy recommendations to the government to design relevant regulations for disaster risk management needs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".