Condominium Development and Gentrifi cation: Th e Relationship Between Policies, Building Activities and Socio-economic Development in Toronto
Bibliographic record
Abstract
Abstract Over the past few years, Toronto has experienced a massive reinvestment into the inner city, mostly in the form of high-rise condominium towers, which was followed by the largest population growth in over 30 years. Th e city that used to praise itself as multicultural, ethnically diverse and socially-mixed, has, as recent studies indicate, become spatially divided into three distinct cities: the constant city of the rich, the shrinking city of middle-income households, and the grow-ing city of concentrated poverty. In this paper we suggest that the condominium towers are a new form of gentrifi cation that contributes to the spatial trifurcation of the city. We call it the condofi cation of Toronto. We start with a discussion of some aspects on gentrifi cation, followed by an analysis of policy documents and reports that have been guiding urban development in Toronto. We then take a look at the incoming condo-dwellers, before we conclude that the City needs to revisit its planning instruments in order to prevent further spatial segregation in Toronto. Key words: gentrifi cation; condominium boom; Toronto; urban politics and planningIntroduction Th ere has been some discussion lately about the changing face of Toronto’s neigh-bourhoods. Th e report
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".