Implementation of Supply Chain and Logistics for Natural Disaster Management in Indonesia: A Smart Governance Perspective
Bibliographic record
Abstract
This research is at the ontological level and sociological level of the implementation of supply chain and logistics equipment for disaster management that are very significant in reducing risk of natural disaster in Indonesia. The problem is very interesting to be analyzed by conducting a descriptive qualitative research. The research used the theory of public policy, smart governance, and supply chain management and logistics. The data were collected using in-depth interview to several key informants, direct observation, and related documentation. The data were analyzed using interactive models, which were data reduction, data display, and data verification, supported by triangulation to obtain validity and reliability. The results were based on ontology, epistemology, and sociology using smart governance perspective by empowering supply chain and logistic to improve disaster management in Indonesia. Vision and mission of public policies related to natural disaster are needed to complete the facilities of prevention, equipment management and logistics supervision, providing information to stakeholders regarding regulations and sanctions in natural disaster that were carried out deliberately and balanced provision of disaster management. Therefore, it will produce a revised and detailed relevant regulation for state agencies as public officials in making regulations on natural disaster and disaster management in Indonesia. The researchers suggest that state institutions must conduct and cover smart governance in making regulations on disaster management.
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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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".