Strategic Analysis of Collaborative Governance for Disaster Management on Forest and Land Fires in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims to understand the strategic analysis of collaborative governance on forest and land fire disasters at the ontological and sociological level that are very significant in reducing risk of natural disasters in Indonesia. The problem is very interesting to be analyzed by conducting a descriptive qualitative research based on theory of public policy, collaborative governance, and strategic management. The data were collected through in-depth interview, observation, and related documentation in forest and land fire cases in Indonesia. The data were analyzed by using interactive models, which are data reduction, data display, data verification, and supported by triangulation. The results were based on ontological and sociological level by using collaborative governance perspective and strategic analysis of internal, external, supporting, and inhibiting factors for reducing disaster risks and improving disaster management. Vision and mission of public policies on disaster management are needed for improving and providing information to stakeholders regarding regulations and sanctions in natural disaster management and produce a revised relevant regulation for state agencies as public officials in making regulations on disaster management in Indonesia.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it