Tightening the Belt: A Comparative Analysis of the Effectiveness of the Urban Growth Boundary in Portland, Oregon and the Ontario Greenbelt in the Greater Toronto Area, Ontario
Bibliographic record
Abstract
As the world’s population continues to increase, development is inevitable. Within North America, this has led to continuing sprawl and expansion of cities, which is resulting in a catastrophic loss of farmland. This is referred to as urban sprawl. Policy has been implemented as a means of combatting urban sprawl, and through the examination of two key greenbelt policies within North America – the Ontario greenbelt in Ontario, Canada and the Urban Growth Boundary in Portland, Oregon – this study aims to examine which is more effective. This study will look at five key variables as a means of analyzing both greenbelt policies and their effectiveness since their implementation. This study will suggest that both the Ontario and Portland greenbelt policies have been moderately effective in controlling urban sprawl, but will also pin point which has been more effective and why. The conclusions of this study are important to future policy implementation and policy review. Further studies should be done to compare other variables, and an addition quantitative analysis would be able to provide statistical comparisons of the two cases chosen.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".