Incorporating a Public Transit Equity Lens in Evacuation Planning
Bibliographic record
Abstract
Disasters caused by extreme weather and human-initiated events continue to disproportionately affect vulnerable and underserved communities. Although these communities often rely on public transit to travel, most North American jurisdictions still lack a fundamental understanding of equity-centered needs or evacuation plans that incorporate public transit use. Consequently, this study conducted a community-centered methodology with eight focus groups in February 2023 among historically underrepresented groups in evacuation planning. Comprising 52 participants in Edmonton, Alberta, the groups included carless residents, people with disabilities, older adults, lower-income households, racial and ethnic minorities, recent immigrants, parents/guardians of young children, and women. Thematic analysis of the focus group data was performed using MAXQDA. Participants identified challenges and concerns related to public transit costs, possible overcrowding, and inadequate assistance services for people with disabilities and the medically fragile during evacuations. The focus groups largely looked to emergency management offices and transportation agencies to ensure public transit reliability, affordability, and accessibility. Surprising references were also made to public transit as a potential tool for building community cohesion and reducing sentiments of anxiety during disasters. Finally, we found that each group had specific insights based on their vulnerability. For example, whereas lower-income households prioritized increased frequency of transit services during emergencies, older adults called for trained medical staff and accessibility features. We offer several policy recommendations to enhance both resilient and equitable evacuation planning.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".