Speculating Social Housing: Mixed-Iicome Public Housing Redevelopment in Toronto's Regent Park and Don Mount Court
Bibliographic record
Abstract
This dissertation develops a multi-dimensional critique of the globally popular "socially mixed" public housing redevelopment approach, drawing on tenant experiences in Toronto's Regent Park and Don Mount Court communities. Socially mixed redevelopment involves the demolition of modernist public housing and its replacement with mixed-income, mixed-use communities; usually redesigned in a neo-traditional style and achieved via public-private partnership. This dissertation addresses three research questions and goals. First, it examines the impacts of redevelopment on tenants, who benefit from much-needed investment but endure hardship associated with relocation, gentrification, and displacement. Second, it critically examines core theoretical and planning ideas (`deconcentration' and `social mix') that serve to justify mixed-income redevelopment. These discourses rest on problematic core assumptions, and promote policies that - as I discover in a meta-analysis of nearly 200 empirical studies - do not deliver on their promises. Third, this research offers a political-economic critique of the significance and future impacts of redevelopment, placing tenant experiences in the context of local, state, and global economic restructuring. Redevelopment is presented as an example of what I call "speculative social welfare," a new and increasingly popular approach for financing former welfare state provisions in the context of global neoliberalism. Speculative social welfare replaces state expenditure with profits derived from gentrification and real estate speculation, relies on the market to allocate former welfare state provisions, entails reduced investment, and intensifies existing patterns of socio-spatial polarization. This research is based on qualitative methods, including in-depth interviews, ethnographic participant observation, and textual analysis. My analysis of redevelopment in Toronto reveals that financial motivations drive policy, trumping loftier planning, design, and equity-oriented goals. From pre-move tenant interviews, I develop a counter-narrative that challenges stigmatizing pro-revitalization rhetoric, and highlights `real' problems in Regent Park that redevelopment is ill-suited to address. Post-move interviews reveal that `old' problems are being reproduced in the mixed community, and highlight the negative political and social impacts of gentrification and displacement. My analysis of Don Mount Court points to the paradoxical outcomes of "New Urbanist" design, and challenges the `myth' of the benevolent middle class in a landscape marked by deeply uneven power relations.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".