A Look at Access to Green Space in Toronto Using the 3-30-300 Rule for Greener Cities
Bibliographic record
Abstract
Many studies have demonstrated the increased use and importance of urban nature and its benefits, especially as it relates to peoples’ health and wellbeing. The 3-30-300 rule was recently introduced by Dr. Cecil Konijnendijk as a guiding principle for urban forest programmes to ensure that all residents have access to trees and green space and their benefits. This rule states that every resident should be able to see at least 3 trees of a decent size from their home, every neighbourhood should have at least 30% canopy cover, and every resident should live within 300 metres from a high-quality park or green space. The status of this rule was evaluated in Toronto, the largest city in Canada, making use of real estate listings for entry-level homes. Of the 180 residences in the municipalities of Toronto (and Mississauga), only 12% met the 3-30-300 rule. When the residences were grouped by listing price, the top third of the most expensive residences had a higher proportion than the other two thirds of residences that met the entire rule and each element of the rule. The correlation between the listing price of a residence and all elements of the rule were statistically significant, indicating that the higher the listing price, the more accessible green space there was. This inequitable access leads to inequitable benefit of green space by residents and further exacerbates the inequity faced by marginalized communities. Given the low proportion of residences in Toronto (and Mississauga) examined for this project that meet the rule, it may not be a feasible goal to have all residences in these municipalities meet the rule as is. Consideration can be given to modifying the thresholds of the rule to ensure that a goal that is aspirational, yet still realistic, for these municipalities results in more green space for as many residents as possible in the long term. It is imperative to address inequalities in access and increase access to green space in urban centres so that all residents are able to experience the numerous benefits provided by urban nature.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".