“SOCIALLY MIXED” PUBLIC HOUSING REDEVELOPMENT AS A DESTIGMATIZATION STRATEGY IN TORONTO'S REGENT PARK
Bibliographic record
Abstract
Abstract Over the last two decades decision makers have sought to address problems with large concentrations of poverty and minority ethnoracial groups in the cities of Western Europe and the Anglo-American world that are the direct result of the manner in which public housing was built in the early postwar era. The United States, Canada, the United Kingdom, and Australia have developed programs that introduce “social mix” into such public housing developments. These initiatives are designed to alter the social dynamics of places with high levels of concentrated poverty and ethnoracial minority groups that are believed to magnify the disadvantages of poverty and marginalization. In this paper, I argue that this is a destigmatization strategy, but not the same kind of destigmatization strategy that has been described in the literature. Using the example of Toronto's Regent Park, a large public housing development near downtown, I develop a research agenda for understanding the gap between a quasi-state agency's efforts to destigmatize public housing sites (“place destigmatization”) and the everyday destigmatization practices and experiences of residents (“personal destigmatization”). The paper begins with a review of the putative mechanisms linking socially mixed public housing redevelopment and outcomes for residents, including social capital, social control, role modelling, and changes to the political economy of place. This review finds little evidence of these effects in the literature. Consequently, I argue for an inductive approach to the study of the outcomes of social mix, rather than the common practice of judging such outcomes against the benchmark of close, intimate relationships between new, middle-class residents and existing public housing residents. I further argue that the “normalization” of the built form that is a major part of socially mixed redevelopment is a form of place destigmatization, and may alter both material practices and representational practices related to stigma, which have very real effects on the everyday experience of residents.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".