Wasted Density? The Impact of Toronto's Residential-Density-Distribution Policies on Public-Transit Use and Walking
Bibliographic record
Abstract
Although the Toronto metropolitan region performs well relative to its North American counterparts in terms of density and public-transit use, it does not derive as much walking and public-transit patronage benefit from its high-residential-density areas as it could. The impact of residential density on journey patterns is limited by an imperfect juxtaposition of density and public-transit service peaks. Another impediment is the difficulty of associating density with other variables needed for it to translate into increased walking and public-transit modal shares. We attribute this situation to insufficient planning capacity owing in large part to generalized neighbourhood opposition to high-density residential developments and disagreement between levels of government. In this paper we both narrate events of relevance to the distribution of high residential density over the last five decades and analyze present relationships between high-density areas and journey patterns. We conclude by discussing the possibility of achieving residential-density layouts and distributions that are more conducive to walking and public-transit use than the tower-in-the-park model and the scattering of high-density pockets, both of which predominate in Toronto.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".