Landscape and Urban Planning 62 (2003) 181–198 Turning brownfields into green space in the City of Toronto
Bibliographic record
Abstract
Since the mid-1980s, policy makers and planners in North America and Europe have been paying significantly more attention to measures designed to foster sustainable development and improve the quality of life in urban areas. One issue that has received widespread political support has been the cleanup and redevelopment of under-utilized brownfield sites in urban areas. In Canada and the US, the focus of policy-making and redevelopment efforts has been on redeveloping brownfield sites for industrial, commercial, or residential uses that provide economic benefits through tax revenues and/or jobs. However, there has been a growing recognition among community groups and environmental organizations that brownfields hold enormous potential for “greening ” city environments, through the implementation of parks, playgrounds, trails, greenways, and other open spaces. The objectives of the current research are to examine the issues, obstacles and processes involved in remediating potentially contaminated urban brownfield sites and converting them into green spaces, to identify the benefits that these green spaces can bring to the community and culture, and to understand the specific planning processes that it involves. Data for this study were collected through a review of 10 pertinent “greening ” case studies and personal interviews with relevant stakeholders. Toronto’s brownfield-to-green space redevelopment experience has implications for cities across North America undergoing brownfield planning and seeking to enhance urban quality of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".