Trying to Smart-In-Up and Cleanup Our Act by Linking Regional Growth Planning, Brownfields Remediation, and Urban Infill in Southern Ontario Cities
Bibliographic record
Abstract
The reuse of brownfields as locations for urban intensification has become a core strategy in government sustainability efforts aimed at remediating pollution, curbing sprawl and prioritizing renewal, regeneration, and retrofitting. In Ontario, Canada’s most populous, industrialized, and brownfield-laden province, a suite of progressive policies and programs have been introduced to not only facilitate the assessment and remediation of the brownfields supply, but to also steer development demand away from peripheral greenfields and towards urban brownfields in a manner that considers a wider regional perspective. This article examines the character and extent of brownfields infill development that has taken place in three Ontario cities (Toronto, Waterloo, and Kingston) since the provincial policy shift in the early 2000s. Using property assessment data and cleanup records, the research finds that redevelopment activity has been extensive in both scale and character, particularly in Toronto where the real estate market has been strong. While the results are promising in terms of government efforts to promote smarter growth that builds “in and up” instead of out, they also reveal that government could be doing more to facilitate redevelopment and influence its sustainability character, particularly in weaker markets.
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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 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".