Deindustrialization, Gentrification, and Displacement in Toronto's Leslieville and South Riverdale
Bibliographic record
Abstract
Leslieville and South Riverdale are traditionally working-class neighbourhoods just east of downtown Toronto. The research in this report employed a mixed-methods case study to analyze deindustrialization, gentrification, and displacement in Toronto’s Leslieville and South Riverdale neighbourhoods. Specifically, this report addressed the following three research objectives: \n \ni.\tDetermine whether, and to what extent, deindustrialization preceded gentrification in Leslieville and South Riverdale \n \nii.\tExamine the relationship between deindustrialization and gentrification in Leslieville and South Riverdale \n \niii.\tIdentify and gauge the effectiveness of past and current policymaker interventions at preventing gentrification-induced displacement in Leslieville and South Riverdale \n \nThis report used quantitative methods to analyze deindustrialization and gentrification in Leslieville and South Riverdale. The research found that the study area deindustrialized to a greater magnitude than Toronto at large. The study area and Toronto’s deindustrialization signified an economic “restructuring” from manufacturing economies to service-based economies. Next, this research determined that the level of gentrification varied by each census tract. Two of the seven census tracts in the study area exhibited every characteristic of gentrification, four census tracts showed most characteristics, while one census tract exemplified a minority of gentrification indicators. Most of the statistical indicators of gentrification in Leslieville and South Riverdale emerged between 2006 and 2011. A correlation analysis of tract-level manufacturing labour shares and gentrification indicators showed there were statistically significant, though complex, correlations between the neighbourhoods’ deindustrialization and gentrification. \n \nThe final component of this research was a content analysis and assessment of anti-displacement policies in the study area. I found that several traditional policies (e.g., rent control, social housing maintenance, developer incentives, rental assistance, etc.) have existed for a long time, including during the study area’s 2006 to 2011 gentrification. On the other hand, more modern policies (e.g., Inclusionary Zoning, co-op expansion, social housing revitalization, rent bank, etc.) have only recently proliferated. Overall, this report found that deindustrialization was a precondition for Leslieville and South Riverdale’s gentrification. Moreover, there were about half the listed anti-displacement policies in place during the study area’s gentrification whereas now most of these anti-displacement policies apply in Leslieville and South Riverdale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".