Enhancing community resilience to ice-jam floods through individuals’ mitigation efforts
Bibliographic record
Abstract
Due to its rapid occurrence and potential for severe damage, ice-jam flooding poses a major threat to riverine communities in cold regions like Canada. Mitigating these flood damages requires public and private strategies. This study investigates the socio-economic and psychological factors influencing residents' ice-jam flood mitigation decisions in Fort McMurray, Canada, by the integration of Protection Motivation Theory (PMT) and the Transtheoretical Model (TTM). To fulfill this goal, we conducted a structured survey among the sampled residents. Key factors such as threat experience appraisal, self-efficacy, and perceived costs turn out to be the important factors in flood mitigation actions. This study suggests that policy approaches should be diversified toward the unique needs of different groups, such as renters and homebuyers, to have a more inclusive policy. Stage-specific interventions are needed, as people at different decision-making stages need strategies that are specifically tailored to adopt protective behaviours. Policies should focus on a tailored approach to enhance self-efficacy by providing specific communication and a step-by-step guide on mitigation strategies. Practical supporting measures should also be pursued as financial incentives and low-cost services that reduce cost barriers and foster the adoption of mitigation actions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".