Wildfire Evacuation Choice-Making among Underserved Groups in Alberta and British Columbia
Bibliographic record
Abstract
Wildfires continue to threaten multiple regions in Canada, and evacuations are often the primary means of ensuring life safety. Understanding how people make decisions before and during wildfire evacuations is thus important in informing preparedness and planning. This research collected survey data between May and July 2023, from residents living in high to moderate fire-risk areas of Alberta and British Columbia ( N = 2,868) to understand their intended evacuation behavior and choice-making during a future wildfire event. Our analysis focuses on underserved groups (people with disabilities, older adults, lower-income households, visible minorities, and carless residents), often neglected in evacuation planning processes. We contribute to the literature by uniquely focusing on decision-making within distinct underserved groups, rather than simply using these identities as variables within a broader model. Estimated logit models offer insight into factors affecting evacuation departure timing, destination and route choices, mode choices, and preferred shelter types. Results suggested that factors such as perceived risk, previous evacuation experiences, and intersecting vulnerabilities have a significant influence on group choices. For example, whereas risk perception significantly influenced evacuation timing among people with disabilities, sociodemographic characteristics were significant in determining shelter choices among older adults. These findings have important implications for enhancing equitable wildfire evacuations, pointing to the need for tailored strategies that consider the needs, barriers, and decision-making patterns of underserved groups. We provide several policy recommendations for local agencies, including ensuring multimodal evacuation plans with transit and shared mobility considerations and providing targeted support for those with intersecting vulnerabilities.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".