Using the 3-30-300 Indicator to Evaluate Green Space Accessibility and Inequalities: A Case Study of Montreal, Canada
Bibliographic record
Abstract
Access to green spaces is essential for promoting public health, reducing inequalities, and fostering urban resilience. This study evaluates the 3-30-300 indicator as a tool for assessing green space accessibility in Montreal, Canada. The framework sets three goals: every resident should see three trees from their home, live in a neighborhood with at least 30% tree canopy, and have a park or green space within 300 m. Using geospatial analysis, this study examines how well these criteria are met across Montreal’s neighborhoods and investigates disparities linked to socio-economic factors. The study reveals a significant variability in the distribution of green spaces across Montreal neighborhoods, as measured by the 3-30-300 metric. Tree canopy coverage ranges from 0.8% to 84%, with a median of 25.7%, while distances to parks vary from adjacent to over 2.4 km. The number of trees around residences is highly skewed, ranging from 0 to 771, reflecting substantial heterogeneity in green space accessibility. Spatial analysis highlights pronounced inequalities, with only 19.4% of neighborhoods meeting all three criteria. Hotspots of compliance are concentrated in peri-central and well-established residential areas in the West and East, while central and peripheral neighborhoods, especially in northeast Montreal, frequently fail to meet the standards. These findings underscore strong spatial disparities in urban green infrastructure, consistent with global studies on inequitable access to green spaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".