Earthquake Threat! Understanding the Intention to Prepare for the Big One
Bibliographic record
Abstract
Knowledge about how hazard-threatened individuals perceive risks and what influences their intentions to prepare is crucial for effective disaster management. We investigated (a) whether residents of objectively higher-risk earthquake areas within a city perceive greater risk, have stronger intentions to prepare, and report more preparation than residents of objectively lower-risk areas, (b) 10 antecedent factors as predictors of the intention to prepare for an earthquake, and (c) whether risk perception mediates the relations between nine antecedent factors and the intention to prepare. Notably, residents of high-risk areas did not express stronger intentions to prepare or report more preparations than did residents of low-risk areas, despite perceiving significantly greater risk. Risk perception mediated the relation between antecedent fatalism and the intention to prepare. Among the policy implications is a clear need for greater education of residents in high-risk earthquake areas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".