Disaster mitigation for vulnerable communities: Technological innovation for sustainable development
Bibliographic record
Abstract
This research aims to analyze thematic trends and tendencies in disaster mitigation literature in Indonesia, with a focus on the linkages between technological innovation and issues affecting vulnerable communities. A bibliometric approach was used on 620 Scopus-indexed articles (2015-2024) through co-occurrence, overlay, and factorial mapping analysis to map the conceptual structure and linkages between themes. The visualization results demonstrate the prevalence of technocratic approaches, which utilize GIS, early warning systems, and remote sensing but are less integrated with social vulnerability issues, such as disabilities, older people, women, and children. This finding indicates a gap between the focus on technological innovation and national policy mandates that emphasize the protection of vulnerable groups in disaster resilience systems. Using the Social Vulnerability and Capability Approach framework, this study highlights the importance of engaging vulnerable communities in the development of adaptive and context-specific technologies. The research recommends exploring cross-sector collaboration in reaching vulnerable groups, as well as encouraging a participatory (bottom-up) approach as the basis for building an inclusive, equitable, and sustainable national disaster resilience system.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".