Automobile Commuting in Suburban High-Rise Condominium Apartments: Examining Transitions toward Suburban Sustainability in Toronto
Bibliographic record
Abstract
While North American suburbs remain largely dispersed and auto-dependent, they are also increasingly heterogeneous. Although some suburbs have long been punctuated with high-rise developments, for instance rental apartments in the Canadian context, there are now a growing number of new high-rise condominium developments in suburban settings in both the US and Canada. While much is known about downtown high-rise condominium developments, there has of yet been little to no analysis of this trend in the suburbs. We offer such an analysis using Statistics Canada census data from 2016 in the Toronto metropolitan area. We focus on commuting patterns as an indicator of auto-dependence to test whether suburbs with larger shares of new high-rise condominium apartments (high-rise condo clusters) exhibit lower shares of auto commuting. The focus on auto-dependence is important because development and land use plans commonly use environmental concerns arising from heavy automobile use as a rationale for high-rise development. Our findings suggest that in Toronto suburban high-rise condo clusters offer a less auto-intensive way of living in the suburbs than traditionally has been the case in the suburban ownership market. However, this seems to be limited to particular demographic groups, such as smaller households; and suburban high-rise condos are not an evident sign of a broader transition toward suburban sustainability among the population as a whole in the Toronto case. The potential for transitions toward suburban sustainability could be enhanced with greater investments in transit infrastructure and building higher density mid-rise and ground-oriented dwellings that accommodate larger households still commonly found in low-density, auto-dependent suburbs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".