Dynamics of Flood and Landslide Risk Governance Policy in Solok Regency, Indonesia
Bibliographic record
Abstract
This research endeavors to elucidate the complexities associated with the policies aimed at mitigating flood and landslide disasters within the Lembah Gumanti region of Solok Regency.The investigation employs a qualitative methodology utilising descriptive techniques, meticulously observing, assessing, and analysing phenomena or issues with fidelity to the prevailing circumstances.The acquisition of data was facilitated through a combination of interviews, observational studies, and documentary analyses.According to the findings of this research, it can be articulated that the execution of flood and landslide disaster mitigation strategies in the Lembah Gumanti region has been undertaken by the Solok Government, bolstered by community engagement through a variety of initiatives.These mitigation strategies can be classified into the following categories: firstly, in terms of structural measures, disaster mitigation is achieved through the development of disaster-resistant infrastructure by the Solok Government; secondly, within the realm of non-structural mitigation, five principal variables emerge: a) The presence of a legal framework governing disaster mitigation; b) The existence of institutional mechanisms, notably the Solok Disaster Management Office, which operates as the command center for disaster management, coordinating with relevant organizational bodies; c) The implementation of an early warning system involving the placement of hazard signs and direct communication appeals through digital media; d) The Solok City Disaster Management Agency has carried out socialization, education, and training aimed at increasing community knowledge, awareness, and capacity, although it still faces many challenges to improve its achievements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".