Planning Healthy Cities: Privately-owned publicly-accessible spaces in Toronto
Bibliographic record
Abstract
Privately-owned publicly-accessible spaces (POPS) are a common feature of the urban landscape in major North American cities, including Toronto, Ontario. These spaces typically accompany urban developments that have requested modifications in planning regulations and represent a type of private-public partnership that is typical of neoliberal city planning policies. There have been limited studies on these spaces and how they may or may not contribute to urban health and wellbeing. There is a significant body of work suggesting that the built environment, especially of urban spaces, is a significant determinant of health and wellbeing of urban residents. Informed by the literature on green public space and health, I developed a framework to evaluate a random selection of 25 Toronto POPS. The three areas of POPS I chose to evaluate are: the level of green space and natural features, the use/functionality, and the accessibility of the space. I also explore what typologies of POPS are most common amongst the 25 spaces I examine. My findings suggest that Toronto POPS, at this time, do not meaningfully support the health and wellbeing of urban populations, as their level of greenery, use/functionality and accessibility is inconsistent and often of poor quality. However, in spite of these findings, there is abundant potential for POPS to become more conducive to urban health and wellbeing goals if the POPS Urban Design Guideline is sufficiently enforced and clarity is provided to whether or not POPS count as a “community benefit” under Section 37 negotiations. It is also recommended that the City of Toronto update their website on POPS, as the information is outdated, incomplete and inconsistent. Improving access to information about where POPS are located in the city may help these spaces be better utilized by members of the public.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".