Towards smart regional growth: institutional complexities and the regional governance of Southern Ontario’s Greenbelt
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it