A global comparison of community-based responses to natural hazards
Bibliographic record
Abstract
Community-based disaster preparedness is an important component of disaster management. Knowledge of interventions that communities utilize in response to hazards is important to develop local-level capacity and increase community resilience. This paper systematically examines empirical information about local-level responses to hazards based on peer-reviewed, published case studies. We developed a data set based on 188 articles providing information from 318 communities from all regions of the world. We classified response examples to address four key questions: (i) what kinds of responses are used by communities all over the world? (ii) Do communities in different parts of the world use different kinds of responses? (iii) Are communities using hazard-specific responses? (iv) Are communities using a multi-hazard approach? We found that within an extensive literature on hazards, there is relatively little empirical information about community-based responses to hazards. Across the world, responses aiming at securing basic human needs are the most frequently reported kinds of responses. Although the notion of community-based disaster preparedness is gaining importance, very few examples of responses that draw on the social fabric of communities are reported. Specific regions of the world are lacking in their use of certain hazard responses classes. Although an all-hazard approach for disaster preparedness is increasingly recommended, there is a lack of multi-hazard response approaches on the local level.
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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".