Beyond Sprawl? Regulating Growth in Southern Ontario: Spotlight on Brampton
Bibliographic record
Abstract
Over the past two decades, the province of Ontario has deliberately upscaled regional governance by creating a firm framework of land use planning. This entailed plans at the super-regional level that protect a large Greenbelt, designate growth centres, and roll out massive transit investment. This laid the foundation for a “real existing regionalism” in which growth management was produced through multiple conversations, contestations, technological change and territorial restructuring. By all accounts, this regime did not produce a perfect safeguard against sprawl – ostensibly the reason for its existence – but it shifted the practices of regional actors in land use and transportation politics and changed the politics around densities. This regime, which was in place for roughly fifteen years and coterminous with the reign of the Liberal Party of Ontario, has now come to an end. A new provincial government under Doug Ford and the Progressive Conservative Party has begun to redraw regional boundaries, to change the discourse around planning and growth management, and to remake transportation policy.This paper will provide a brief history and assessment of the shifts in recent Ontario sprawl-management regimes and will attempt an early analysis of the consequences for regional governance in the Greater Golden Horseshoe region of Ontario. In order to highlight the cutting-edge dynamics and consequences of these variegated regional governance regimes, we will have a focus on the suburban municipality of Brampton in the Greater Toronto Area. Brampton has a particular socio-economic composition that has the ability to reflect the ongoing political processes and socio-economic relations pertaining to the transformation of land usage over the course of suburban development in the GTA. We will focus on the regional aspect of this peripheral expansion, its governance as well as its link to the housing market dynamics.
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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 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".