Transportation Justice in Suburbia - A Case Study of Downtown Planning Initiatives
Bibliographic record
Abstract
Post-war suburban development has, for years, embraced an automobile-oriented growth pattern through the separation of land uses and low-density built forms that are attuned to the convenience of the car. Suburban streetscapes have therefore had very little space for other transportation modes to flourish. Automobile-dependency is in fact a cultural norm, particularly among the middle class. In recent years, Ontario provincial planning and growth policies have addressed the concerns put forth by automobile-dependency and sprawl, mandating intensification of built-forms that facilitate a multi-modal shift aimed towards more sustainable transportation options, such as walking, cycling, and transit.Such a framework could create a more equitable transportation network that caters to people from multiple socio-economic backgrounds, especially those who are limited in their opportunities to afford or use vehicles. However, transportation justice, though it serves as an indirect by-product of a multi-modal balance, has been negated and overlooked as a key growth framework. Alas, intensification strategies have resulted in the growth of suburban downtowns as the primary growth model to facilitate such a balanced modal split, but there is little empirical evidence to suggest that such a framework is successful in reducing the rate of reliance on vehicles. This paper evaluates downtown planning strategies and concludes that although they may facilitate a balanced modal split within the downtown, such a pattern does not produce a significant impact on the rest ofSuburbia, where automobile dependency is most prevalent.
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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 | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".