Urban Restructuring, Homelessness, and Collective Action in Toronto, 1980–2003
Bibliographic record
Abstract
This article explores the links between urban restructuring, homelessness, and collective action in Toronto in the 1980s and 1990s. In Toronto, as elsewhere, urban restructuring at this time comprised a series of interconnected political-economic and spatial shifts, including economic and occupation change, gentrification, neo-liberal welfare state reform, and urban entrepreneurialism. Jointly, these political-economic shifts were implicated in the production and consolidation of new forms of socio-spatial polarization and segregation that dramatically changed the landscape of urban poverty. One of the most visible manifestations of the uneven effects of restructuring was the emergence and consolidation of mass homelessness. This changing landscape of poverty, in turn, produced a new landscape of political activism. It is this contested landscape that I explore in this article through a focus on homelessness as a primary mobilizing issue in opposition to restructuring during this key period in Toronto’s transition into a second-tier world city. I argue that urban restructuring, homelessness, and the dynamics of collective action were linked in two important ways. First, collective advocates and activists defined the crisis of homelessness as a direct effect of urban restructuring; in this way collective action mobilized to defend the interests of homeless people was simultaneously a collective struggle to contest urban restructuring. Second, the politics of restructuring directly informed the dynamics of collective action over time, influencing their organizational, strategic, and tactical dimensions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".