Implementation of the Resilient Village at Gunung Geulis Village, Sukaraja Sub-regency, Bogor, West Java
Bibliographic record
Abstract
This study aims to analyze (1) the participation of the community of Gunung Geulis Village, particularly vulnerable groups, in managing resources in order to reduce disaster risk; (2) increasing the capacity of citizens and officials on disaster management and (3) the performance of the Village DRR forum. This study uses a qualitative method. The subjects of this study were Gunung Geulis village secretaries using the interview method. The results showed (1) personnel from the disaster management forum planted trees on landslides prone area and made archery embankments, (2) Gunung Geulis Village as a program implementer of Disaster Resilient Village had been actively conducting routine and ongoing training every once a month in an effort capacity development on disaster management, (3) the Disaster Risk Reduction (DRR) forum of Gunung Geulis village has carried out passive mitigation efforts such as making mapping and analysis of disaster risk, even though there is no document compiled. DRR personnel also carry out mitigation and early warning efforts through information through pamphlets, brochures, and other village meetings. In addition, active mitigation efforts have been made by the DRR team.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".