On the Margins of Gentrification: The Production and Governance of Suburban 'Decline' in Toronto's Inner Suburbs
Bibliographic record
Abstract
In many North American cities, the last two decades have witnessed not only the large-scale return of investment priorities to central cities, but also a rise in the suburbanization of poverty. This dissertation examines the problem of ‘suburban decline’ by investigating how it is produced, its effects on sub/urban populations, and the responses that it generates. I interrogate the processes through which relations of political-economic and cultural dominance are obscured, enacted, and reproduced through the production of sub/urban investment and decline. In doing so, I identify the ongoing colonial practices, classism, and systemic racism that are embedded into the neoliberal state and through it, the production and management of marginalized spaces and populations. I trace the mechanisms and techniques through which this power is mobilized to control populations and contain dissent. Finally, I demonstrate, through moments of social struggle, that growing unrest and changing demographics related to ‘suburban decline’ represent crises to the dominant structures of power. In this three-paper dissertation I document processes of political-economic and racialized marginalization in Scarborough, an inner suburb of Toronto. My analysis centres around three key moments of major contestation that are also central to social and political change in this area of the city: a failed mobilization to save community space that had taken root in a local retail mall; the implementation of a motel shelter system; the roll-out of the priority neighbourhood strategy for social investment and community governance. Highlighting struggles over suburban space and belonging, I trace the growth and decline of Toronto’s postwar suburbs, through the lens of Southeast Scarborough. I probe the tensions arising from suburban decline by excavating responses from the state at multiple levels, popular media, social agencies, faith groups, and residents. More broadly these struggles inform key dynamics that are central to contemporary transformations in sub/urban socio-spatial relationships: the production of racialized space; the targeting of poor communities; and devolution of governance responsibilities through third-sector organizations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".