Regional growth planning in practice: an examination of brownfield redevelopment activity in Guelph and St. Catherines
Bibliographic record
Abstract
For many cities, brownfield properties are an underutilized land resource. As a part of a comprehensive policy approach, A Place to Grow: A Growth Plan for the Greater Golden Horseshoe, encouraged development on brownfield sites to fulfill urban intensification goals and support regional sustainable growth objectives. Distinguishing between policy and practice, this study examines the extent to which brownfield redevelopment activity in two mid-sized cities, Guelph and St. Catharines, follows sustainable growth objectives and the implements the intent of the Growth Plan. Results were drawn from analysis of Records of Site Condition (RSCs) filed on the Province of Ontario’s Environmental Site Registry, Community Improvement Plans and visual site inspections. Overall, brownfield redevelopment occurred in locations identified by the Growth Plan and achieved infill purposes, although the abundance of greenfield land in Guelph presented significant challenges. While market mechanisms remained a determining factor in both cities, St. Catharines appeared to better influence sustainable character in redevelopment activities. Recommendations to facilitate brownfield redevelopment and support sustainable growth objectives are provided. Keywords: brownfields; growth plan, redevelopment, infill, contamination, sustainability, community improvement plan
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".