Communicating risk for vulnerable groups: a case study of the Mano community’s strategies for collective knowledge to action
Bibliographic record
Abstract
This paper discusses the emergent interest in risk communication as a strategy for disaster risk reduction. Communication plays an essential role in understanding risk, but studies suggest that people often do not respond in the way that risk experts anticipate. For risk communication to be effective, vulnerable communities need to understand risk within the local context as well as in terms of sustainability. Risk messages offer communities a way to enhance their collective knowledge of existing vulnerabilities, leading them towards alternative solutions for action. A longitudinal study of the Mano community development approach and its recovery from the 1995 Kobe earthquake illustrates how risk communication dynamics contributed to the community’s sustainable risk reduction. The study concludes that risk communication is a collaborative way for a community to work with risk experts, own their risk information, influence existing policies and practices, develop solutions to reduce vulnerability, and ultimately enhance a community’s capacity for managing future risk.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.038 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".