Suburban Mixed-Use Centres and Urban Dispersion: What Difference do they Make?
Bibliographic record
Abstract
In a context of growing car dependency and suburban sprawl, planners search for ways of intensifying urban development and reducing reliance on the automobile. The creation of planned mixed-use centres intended to become hubs of transit and pedestrian movement within the dispersed suburban environment represents one such intensification strategy. I investigate three suburban mixed-use centres in the Greater Toronto Area, selected for their advanced level of development, and identify the planning rationales and objectives that have led to their creation. To verify the extent to which they meet their intensification goal, I monitor the three selected centres' level of development, modal split, land-use pattern, inner synergy, and inner movements. Findings are mixed. If the suburban centres have been successful in attracting development and attaining levels of transit use, pedestrian movement and inner synergy exceeding those of the typical suburban area, they are not as distinct from the remainder of the suburb as intended and thus fall short from their planning objectives. I conclude that a strategy combining the creation of nodes (such as suburban mixed-use centres) with high-density, transit-oriented corridors within the suburban environment would be more effective in bringing intensification to this portion of the metropolitan region.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".