Quality of Place and the Rescaling of Urban Governance: The Case of Toronto
Bibliographic record
Abstract
Globalization is best described as a complex process embodying conditions of instant communication and the rapid movement of people, goods, and ideas. Aligned with this process is a reorganization of state and society that some geographers have referred to as “rescaling”—a reconfiguration of the spatial scales at which governance occurs. The emerging landscapes of a rescaled global economy reveal not a diminishing role for the local, but rather the resurgence of place—cities—as deterritorialized centres of global control. The prominence of these command and control nodes within a global post-industrial economy is increasingly being linked to discourses of economic efficiency. Using the case of Toronto, Canada, the authors interpret changing governance structures as evidence of a rescaling process that has seen resources and responsibilities move in opposite directions, to the detriment of the quality of place in the city. Rhetorical calls for global competitiveness have led to a withering of the state’s role in providing the context for the emergence of locally enriched social and cultural environments. The case of Toronto reveals a spatial paradox where changes in governance aimed at enhancing global competitiveness have actually diminished the local qualities cities depend upon to sustain such advantage. None the less, economic competitiveness and quality of place need not necessarily work in opposition, and we advance a general planning framework to balance these desires.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".