Bibliographic record
Abstract
This paper presents a detailed analysis of the 2022 earthquake in West Java, Indonesia, shedding light on the disaster risk reduction (DRR) strategies before and after the event. The earthquake, with a magnitude of 5.6, struck the West Java Province on November 21, 2022, causing significant devastation and loss of life. Drawing from various sources including media reports, official statements, and scholarly articles, this study examines the immediate impact of the earthquake, consequential hazards such as landslides and flash floods, and the overall disaster management and preparedness framework in West Java. The research highlights the challenges faced by the government and communities in responding to the disaster, including issues related to early warning systems, evacuation procedures, and shelter provision. It also discusses the role of adaptive resilience and coping responses in mitigating the effects of the earthquake, as well as the importance of ongoing research and innovation in DRR efforts. Through a comparative analysis of past disasters and recovery efforts in Indonesia, the paper underscores the progress made in disaster management over the years while also identifying areas that require further attention and improvement. The findings contribute to the ongoing discourse on disaster resilience and offer insights for enhancing future DRR strategies in Indonesia and similar high-risk regions.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".