Regional Planning and Urban Revitalization in Mid-Sized Cities: A Case Study on Downtown Guelph
Bibliographic record
Abstract
With over a decade having passed since the inception of the provincially led growth plan in Ontario, there is an opportunity to explore how cities have adapted to meet the challenges of this regional-scale plan. The Growth Plan for the Greater Golden Horseshoe seeks to mitigate the negative eff ects of decades of sprawling development by focusing on building dense, urban, transit-connected communities. While the growth plan has a primary focus on municipalities in the Greater Toronto Area, it is also inclusive of smaller urban centres that sit outside of the province’s Greenbelt. Th ese mid-sized cities have a history of downtown decline and dispersed urban form. With the inclusion of mid-sized cities in the growth plan, however, there is an opportunity to explore the strategies smaller municipalities are using to attract public and private investment and achieve residential and employment provincial targets in their core areas by 2041. Th rough a case study approach, focused on downtown Guelph, Ontario, this paper argues that the growth plan can serve as a catalyst to alter the planning paradigm in mid-sized cities, and that through locally led community planning efforts, and a range of site-specific incentives, mid-sized cities can begin to revitalize their downtowns and reverse core area decline.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".