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Record W7132971436

Neighbours, Not Virus – Crises, Carcerality and Care in Toronto’s Covid-19 Encampments

2023· dissertation· W7132971436 on OpenAlexaboutno aff
Mobólúwajídìde David Joseph

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownTrespassCriminalizationPandemicInsiderEntitlement (fair division)Mental healthJungle
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.017
Scholarly communication0.0080.003
Open science0.0010.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.053
GPT teacher head0.423
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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