Towards smart regional growth: institutional complexities and the regional governance of Southern Ontario’s Greenbelt
Bibliographic record
Abstract
The task of developing regional greenbelts poses multidimensional challenges to policymakers. Unlike their early 20th-century predecessors, these greenspaces incorporate multiple functions including growth management, farmland and environmental protection, and increasing economic competitiveness. This regional and multifunctional approach to greenbelt management involves considerable governance complexities, as an increasing number of policy fields such as economic growth, agriculture, housing, nature conservation, different policy levels and various territorial jurisdictions become involved in policy implementation. However, institutional dimensions of contemporary greenbelt governance are hardly reflected within the literature. This is also the case for the Greater Golden Horseshoe region in Southern Ontario, Canada, where a regional Greenbelt Plan was implemented in 2005. By engaging with institutional perspectives on regional governance, we analyse how the governance of regional greenbelts and smart growth have been influenced by vertical, horizontal and territorial coordination challenges and politics at the provincial and local levels. We conclude that despite provincial government intervention in regional planning, the impact of market pressures, growth coalitions and institutional coordination problems prevent growth management policies from delivering the significant changes promised by the Ontario government.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".