Mixed-use intensification in Planning and Development: Transportation in the Greater Toronto Area (GTA)
Bibliographic record
Abstract
Urbanization in the Greater Toronto Area (GTA) has increasingly focused on intensifying its built environment to promote sustainable urban growth. This approach emphasizes mixed-use development, integrating various land uses to encourage sustainable modes of transportation while promoting social and housing diversity. However, the literature indicates that reducing car dependency is challenging, especially in low-density environments where private transportation is the most convenient option. Additionally, critiques highlight the limitations of mixed-use intensification projects in fostering diversity. This paper examines the practice of mixed-use intensification in the GTA through a mixed-method approach, including a linear regression analysis and interviews with residents of mixed-use projects. The research aims to assess the effectiveness of reducing automobility and creating an inclusive urban environment under the mixed-use scheme. The findings reveal the limitations of mixed-use intensification in addressing suburban car culture and provide insights into residents' perspectives on these projects. The research highlights the importance of studying mixed-use intensification for future planning and development initiatives, offering valuable insights into their effectiveness, challenges, and areas for improvement.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".