The Ontario Greenbelt: Shifting the Scales of the Sustainability Fix?
Bibliographic record
Abstract
The government of Ontario, Canada, has passed legislation to protect large parts of the southern portions of the province from most development. An extensive plan for a Greenbelt that surrounds the Greater Toronto Area and other regional growth centers was introduced. This article looks at the new policy as a spatial strategy that shifts the scales of environmental and growth management policy in Ontario. The legislation also sets the framework for a state spatial project; that is, a set of changes in how the regional state internally operates. The current Greenbelt legislation is a new step in a longer term development by which governments in Ontario have attempted to regulate the relationships between cities and regions, town and hinterland. Overlapping strongly with what is usually called the Toronto bioregion between the Niagara Escarpment, Oak Ridges Moraine, and Lake Ontario, the Greenbelt reorganizes space in southern Ontario in ways that would further ecosystem policies and practices in the area. Theoretically guided by newer debates on rescaling and regionalism, and based on close reading of the planning and policy documents on the Greenbelt as well as a series of expert interviews, we argue that the current Greenbelt legislation is an act of up-scaling traditional urban-regional regulation in southern Ontario, which we shall call extended metropolitanization. Such rescaling recasts traditional political conflicts in new terms. We conclude that extended metropolitanization in southern Ontario has been a process that has brought nature, the state, and governance together into a new regional sustainability fix.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".