Mother Nature’s Fury: Antagonist Metaphors for Natural Disasters Increase Forecasts of Their Severity and Encourage Evacuation
Why this work is in the frame
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Bibliographic record
Abstract
Natural disasters are often described as having antagonistic qualities (e.g., wildfires ravage). The information deficit model presumes that when people assess the risk of weather hazards, they ignore irrelevant metaphoric descriptors. However, metaphoric frames affect reasoning. The current research assessed whether antagonist metaphors for natural disasters affect perceptions of the risk they pose. Three studies ( N = 1,936) demonstrated that participants forecasted an antagonist-framed natural hazard as being more severe, and intended to evacuate more often, than a literal-framed natural hazard. Thus, the metaphorical language used to discuss natural disasters deserves consideration in the development of effective risk communication.
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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 | 0.000 |
| 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 it