Exploring the 15-minute City: Access Sufficiency, Travel Behaviour, and Perceptions of Accessibility in the City of Toronto
Bibliographic record
Abstract
The populations of cities have been increasing, so ensuring that cities provide a high quality of life for their residents has become an increasingly important goal in urban planning. Compact cities are considered the most sustainable urban form which has led to the recent popularity of the 15-minute City concept. Using the City of Toronto as a case study, this thesis explores different aspects of the 15-minute City to determine the relationship between 15-minute accessibility and travel behaviour in a North American context. The results suggest that residents of Toronto tend to use slower more sustainable modes of transport if they provide complete sufficient 15-minute access to destinations. Additionally, perceptions of accessibility improve as 15-minute access becomes more complete. However, several issues, notably Toronto’s current urban form, prevent the 15-minute City from being realized in all parts of the city.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".