State rescaling in practice: urban governance reform in Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines governance reform in the Toronto area through the lens of literature on state rescaling. Over the past 20 years, Toronto has been the site of numerous initiatives to shift the spatial contours of urban governance. Viewing these as varied manifestations of the practice of state rescaling allows for a broad analysis of empirical patterns and trends, and informs the empirically underdeveloped literature on state rescaling with new evidence. The paper presents an inductive, historical, and agent-centered account of governance reform in Toronto. It finds that while state rescaling often originates as a response to the policy challenges arising from social change, economic restructuring, and urban growth, actual rescaling practices are shaped by a variety of locally contingent institutional and political factors. It also argues that in recent years, the long-standing practice of jurisdictional rescaling, which involves comprehensive scalar shifts in governing authority, has largely been replaced by task-specific rescaling, characterized by problem-driven initiatives to mobilize governing authority across multiple governing scales. The paper discusses the causes and the broader implications of this shift.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it