Pandemic Encampments: A Case Study of Toronto’s Homeless Encampments (Through a Human Rights-based Approach) During the COVID-19 Pandemic
Bibliographic record
Abstract
Cities across North America have seen an increase in groups of people experiencing unsheltered homelessness together. The City of Toronto specifically is experiencing one of the deepest housing crises to date. Before the COVID-19 pandemic, many people lived in encampments. However, they were largely invisible as they were pushed out of sight by law-enforcement and criminalized by municipal legislation. The City’s response to encampments during the pandemic has demonstrate systemic violence, criminalization, and displacement of unhoused residents. This paper aims to analyze the role of homeless encampments through a human rights-based approach to housing by analyzing encampments during the COVID-19 pandemic. Through this paper, I aim to establish that encampments are sites of resistance to state violence and insufficient interventions. The resistance of unhoused residents and sites of encampments offers a radical change in perspective to housing and a call to action– one that is based in human rights.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".