Urban brownfields redevelopment in Canada: the role of local government
Bibliographic record
Abstract
As in many U.S. and European cities, the legacy of a negligent past has left scars on Canada's urban landscape in the form of numerous under‐used industrial and commercial brownfield sites. While governments in the U.S. and Europe have implemented a variety of policies and programs to help developers overcome the costs and risks associated with redeveloping these sites, there continues to be apprehension among stakeholders in Canada that efforts implemented by the different levels of government here have been deficient, fragmented and piecemeal in comparison. This paper examines the nature of the brownfields problem in Canadian cities and investigates the role of local governments in managing these problems ‘on the ground’. Survey data from 24 cities, coupled with information gathered from four site visitations, reveal that brownfields are indeed a problem for many cities. The data suggest that even though perceptions of what is needed to better manage the problem locally are relatively similar throughout the country, managerial efforts remain disparate and somewhat limited because of diverse provincial policies and variable property 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".