Neighbours, Not Virus – Crises, Carcerality and Care in Toronto’s Covid-19 Encampments
Bibliographic record
Abstract
Against the background of the Covid-19 pandemic and other ongoing and interlinked crises: housing, mental health and the opioid epidemic, encampments of unhoused persons proliferated across Toronto’s downtown parks. Following the end of shelter-in-place directives, and a push to return to business as usual, the City of Toronto announced its ‘Pathway Inside’ program as an ostensibly caring response to the encampments. This program was, however, coupled with Notices of Trespass issued to encampment residents and the threat of carceral violence should they fail to comply. This thesis investigates the city’s clearings of public parks in 2021 and its enactment of what I call ‘care-violence’ on the unhoused through the denigration and criminalization of encampment residents during the Covid-19 pandemic. However, rather than regarding this as a brief moment of violence, this thesis takes the City of Toronto’s regulation and management as emerging out of a longer history of social discipline and carcerality. Against this, I track praxes of care oriented toward mutual survival as present in encampments and the alimentary infrastructures built by activists and advocates in support of them. I take up Lauren Berlant’s ‘infrastructure of the commons’ and Ruth Wilson Gilmore’s ‘life in rehearsal’ as frames to better situate these struggles within an abolitionist horizon.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".