A Scan of Ontario Cities’ COVID-19 Policies and their Impacts on People Living in Homelessness
Bibliographic record
Abstract
This article summarizes findings from a scan of COVID-19 policies in Ontario that impact individuals experiencing homelessness. We collected and analyzed policy data between March 2020 and August 2020, from 10 cities, including all municipal-level restrictions and public health measures implemented in response to COVID-19. From our scan, we found that 161 policies had direct or indirect implications for people living in homelessness. These policies were organized into categories that describe ‘where’ effects were seen – which most often relate to requirements for physical distancing. Some of the most obvious impacts relate to reduced access to needed services and supports in light of non-essential business closures and other service disruptions. Other key impacts relate to the use of public spaces during the pandemic - including access to sanitation facilities, encampment bylaws, changes to public transit services, quarantine and isolation mandates, and the impact of a province-wide stay-at-home order. Overall, in reviewing local responses to the pandemic, it is critical to consider the disproportionate impacts of restrictive public health measures on already marginalized groups and continue to learn about strategies that aim to protect all members of society.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".