Unseen Vulnerabilities: Emergency Planning for Carless and Vulnerable Populations in Ontario
Bibliographic record
Abstract
In 2005, Hurricane Katrina made impact on southern Louisiana, devastating the city of New Orleans and surrounding communities. The high death toll of the hurricane revealed problems with public emergency planning, as many carless and vulnerable people were unable to evacuate New Orleans before the arrival of the hurricane, contributing to the high death toll. In recent years, scholarship has developed around emergency planning for carless and vulnerable populations in order to determine the most effective policy recommendations to improve evacuations plans for these populations. As climate change continues to occur around the world, the frequency and severity of severe weather events will increase. In 2023, Canada has experienced multiple severe weather events that have required evacuations. This paper assesses municipal emergency plans in Ontario to determine whether provincial and municipal officials have taken carless and vulnerable populations into account when planning for emergencies and potential evacuations. To complete the assessment, this paper utilizes a theoretical framework for analyzing emergency plans developed by Renne and Mayorga (2022). By evaluating the plans across five dimensions provided by the framework, this paper concludes that emergency planning for carless and vulnerable populations in Ontario is generally overlooked in emergency planning. To correct this oversight, this paper provides policy recommendations, including implementing vulnerable persons registries supported by emergency services, determining fixed locations for evacuation centres, and requiring planning for carless and vulnerable populations through provincial legislation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".