Toronto Inc? Planning the Competitive City in the New Toronto
Bibliographic record
Abstract
This paper analyses recent developments in urban planning in the City of Toronto. A municipality of 2.4 million inhabitants that makes up the inner half of the Greater Toronto Area, the City of Toronto was consolidated from seven municipalities in 1998. Planning practice, discourse, and “vision” in the new City of Toronto are shaped by the city’s bid for the 2008 Olympics, related proposals for waterfront redevelopment, and preparations for a new official plan. In the context of comparative debates on trends in local governance, we see current planning strategies in Toronto as one of several strategic sites in which Toronto is consolidated into a “competitive city.” Historically, the formation of the competitive city in Toronto must be seen as a result of the impasse of postwar metropolitan planning in the early 1970s, the sociospatial limitations of downtown urban reform politics in the 1970s and 1980s, and the neoliberal restructuring and rescaling of the local state in the 1990s. Theoretically, we draw on the global city research paradigm, regime and regulation theory, and neo‐Gramscian urban political theory to suggest that planning the competitive city signals shifts in the sociopolitical alliances, ideological forms, and dominant strategies that regulate global‐city formation. These constellations and strategies threaten to reconstitute bourgeois hegemony in Toronto with a series of claims to urbanity.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".