Experts at risk: The influence of expertise on conceptualising multi-hazard risk perception and preparedness in Squamish, Canada
Bibliographic record
Abstract
Considering the growing threats posed by the dual forces of the climate crisis and rapid urbanisation, risk management and emergency preparedness strategies must evolve to address the complex realities of multi-hazard environments. Such strategies are sometimes created and designed by natural hazard ‘experts’ without involvement from ‘non-experts.’ This study examines how expertise influences individuals’ risk perceptions and preparedness actions for multiple hazard types in Squamish, Canada – a community exposed to a range of natural hazards including floods, wildfires, earthquakes, and landslides. Utilising a mixed-methods approach combining surveys (n=159) and interviews (n=27), we compared the perceptions and preparedness actions of natural hazard experts and non-experts. Our findings reveal that experts and non-experts conceptualise “risk” differently, and that risk perceptions vary between different hazard types. Surprisingly, we found no significant differences in overall risk perception or disaster preparedness actions between experts and non-experts. These results suggest that natural hazard expertise, as conventionally defined, does not significantly enhance individual preparedness in multi-hazard contexts. The study underscores the importance of integrating both expert and non-expert perspectives in disaster risk reduction strategies.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".