Rise Overrun: Condoization, Gentrification, and the Changing Political Economy of Renting in Toronto
Bibliographic record
Abstract
Privately owned high-rise condominiums have been increasing as a proportion of all housing units built in the Greater Toronto Area for many decades. This has inspired a growing literature theorizing both “condoism” as an emergent planning-development regime and the implications of “condoization” and “condofication” for urban governance and everyday life in cities like Toronto. Building on this literature, this article assesses the implications of Toronto’s increasing reliance on (mainly vertical) condominium development for the socio-spatial transformation of the housing market, particularly for renters. Analyzing time-series data from Canada Mortgage and Housing Corporation and the Census of Canada to quantify the effects of the city’s condoization, we answer three key questions: How important is condominium development for understanding the restructuring of Toronto’s economy? How has condoization contributed to the ongoing gentrification of Toronto’s inner city? How is condoization restructuring Toronto’s rental market? Building on previous research categorizing and mapping the gentrification of Toronto’s inner city, we find that condoization is an increasingly defining element restructuring the city’s rental market, while this restructuring also plays a central role in the advancing gentrification of the city’s core.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".