Evidence-informed recommendations for municipal supports of people experiencing homelessness during extreme weather events
Bibliographic record
Abstract
Municipalities are increasingly faced with a growing climate justice issue: the intersection of rising housing vulnerabilities (e.g. increased rates of homelessness) and climate hazards (e.g. extreme weather events), which disproportionately impact people experiencing homelessness (PEH). Despite the strong role that municipalities have to play in supporting PEH during extreme weather events, municipal interventions remain poorly researched. To investigate such interventions, this paper reports on a broad policy scan of 40 Canadian and international municipalities and a peer-reviewed and practice-based literature review of interventions to support PEH during three recurring extreme weather events: extreme heat, extreme cold, or poor air quality associated with wildfire smoke. Findings show that interventions should focus on preventive responses, such as housing strategies, and also include adaptive crisis management strategies such as the provision of accessible and inclusive shelter accommodations and warming or cooling spaces; communication strategies; outreach services; and the provision of resources. This paper shares key recommendations surrounding the implementation and development of these strategies. Recommendations include factors to consider within emergency sheltering spaces, considerations for communication methods and outreach services, and processes significant to the development of contextually relevant and responsive supports, such as the usage of participatory processes with PEH when developing interventions.
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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 | 0.054 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".