Schoolyard Parks : How the Establishment of a Formal Partnership Between the Toronto District School Board and the City of Toronto to Green Schoolyards Can Increase Access to Public Park Space Across the City
Bibliographic record
Abstract
Many built-up and high-density cities such as the City of Toronto are beginning to look at new strategies to increase parkland at a time when land is scarcer and more expensive to acquire. Schoolyards have been identified as underutilized public resources since many of them are deteriorating and predominantly asphalt. Some cities have established initiatives that revitalize public schoolyards into green spaces for student and community use, defined generally as ‘schoolyard parks’. Such initiatives are based upon public-private partnerships between city governments, school boards, park departments, and other stakeholders. This paper uses spatial analysis to estimate how much parkland Toronto District School Board schoolyards could contribute to Toronto’s park system if they were converted to schoolyard parks. It also reviews four schoolyard park programs in different cities to determine what kind of program structure would best suit Toronto, and it provides recommendations on how to implement such an initiative. Key words: schoolyards, parks, green spaces, partnership, underutilized schools
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".