Governing Greenbelts in Southern Ontario and the Frankfurt Rhine-Main Region: an Institutional Perspective
Bibliographic record
Abstract
In the last 30 years, a new generation of greenbelts has emerged in planning practice. These recent greenbelts have multi-functional policy goals and are often part of comprehensive regional land-use planning frameworks designed to manage regional growth more effectively. However, these regional greenbelts are increasingly under threat from suburban low-density development and the expansion of infrastructure networks, and their governance is embedded in complex institutional arrangements. These evolving circumstances create considerable challenges for policymakers seeking solutions to effectively govern these regional greenbelts. This study explores how institutional arrangements shape the governance of regional greenbelts in the Greater Golden Horseshoe region of Southern Ontario, Canada, and in the Frankfurt Rhine-Main region, Germany, as well as how these greenspaces could be more effectively managed in the future. The study shows that addressing the complex interactions between institutions and stakeholders involved in greenbelt management creates significant difficulties in coordinating policy implementation across different policy levels, policy fields and jurisdictions. Thus, this study reveals that the current institutional arrangements supporting new generation greenbelts cannot fully deliver on their ambitious policy objectives. To overcome these problems and to effectively manage these greenspaces, this study points to institutional design reforms needed for a new generation of greenbelts.
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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 | 0.007 | 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) | 0.004 | 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".