Reconstructing Social Housing: The Socio-spatial Effects of Welfare State Transformation in Toronto's Regent Park
Bibliographic record
Abstract
This dissertation charts the socio-spatial impacts of welfare state reform on the landscape of public housing, and the transformation in modes of governance that mark the shift from a welfare state to a more neoliberal regime. Specifically, it explores the processes and implications of socially-mixed public housing redevelopment, and the ways in which urban planning is used as a social fix. Its focus is Toronto's Regent Park, Canada's first and largest government housing project, which is currently undergoing a $1.75 billion overhaul in which the entire 69-acre site will be razed and rebuilt. Regent Park is widely considered to be a mid-century planning project that failed. Its redevelopment, premised upon the planning philosophy of social mix - diversity of income groups and housing tenure - is intended to be a reversal of poor design and residential segregation. The redevelopment is also financially motivated, unrolling against a backdrop of encroaching neoliberal reform.The appeal to a "healthy" community is central to the re-imagined Regent Park. I explore Regent Park's redevelopment as an exercise in community building and as a political-economic project, with a focus on the rhetoric that underlies these motivations, and on the ways in which these interventions have material effects on the built environment, and on the people who live there. This dissertation, then, is an exploration of attempts to ensure physical and social well-being through an engineering of urban space, and the forms of governance made manifest in these attempts. As such, it is also an exploration of the intersection of two political rationalities. In the current moment, private entities are increasingly called upon to provide public services, in tandem with or in lieu of the state. In Regent Park, the ideals of neoliberal city-building sometimes clash with transforming, but still-existing, bureaucratic welfarist institutions. This thesis examines the ways in which discourses of redevelopment are in tension with the operating of the bureaucratic welfare state, and the ways in which social housing residents experience these competing rationalities and contradictions in political agency.
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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 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".