Enhancing Community Resilience to Ice-Jam Floods: Insights from Socioeconomic and Psychological Factors in Fort McMurray, Canada
Bibliographic record
Abstract
Ice-jam floods present a real risk for riverine communities in cold climate regions through their often-sudden but always potentially destructive nature. This fact makes the analysis of the driving factors for residents in adopting measures against ice-jam flood hazards in the city of Fort McMurray, Canada, very relevant. By employing a structured survey that integrates the Protection Motivation Theory and the Transtheoretical Model, we identify self-efficacy, threat experience appraisal, and perceived costs as some of the key drivers influencing protective behaviors. The results also call for stage-specific, tailored interventions in concert with variation in readiness to act. Our findings clearly indicate that to realize the hoped-for increase in the adoption rate, policy approaches have to be directed to address the cost barrier, develop self-efficacy through appropriate communication strategies, and consider the peculiarities of various community groups, such as renters and transient populations. By proposing public policies, this work demonstrates how these strategies can be utilized as inputs for quantitative modeling approaches, such as agent-based modeling, to evaluate their impact on community-wide flood risk management. This research underlines the importance of integrating behavioral insights with advanced quantitative modeling tools in designing and implementing better flood risk management strategies that promote more resilient communities.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".