Enduring Features of the North American Suburb: Built Form, Automobile Orientation, Suburban Culture and Political Mobilization
Bibliographic record
Abstract
As any social phenomenon, the evolution of suburbs can be seen as at the confluence of two contradictory sets of forces. There are first forces of change, which propel suburbs in new directions. Much of the present literature on suburbs highlights suburban transitions in the form of social and economic diversification, and of new forms of development. The article attempts to rebalance the discourse on suburbs by emphasizing forces of durability. It does not deny the importance of observed suburban transitions, but argues that there is, at the heart of North American suburbs, an enduring automobility-induced transportation dynamic, which reverberates on most aspects of suburbs. The article explores the mechanisms undergirding suburban durability by linking the suburban transportation dynamic to the self-reproductive effects of a suburban lifestyle and culture and their political manifestations. These forces impede planning attempts to transform suburbs in ways that make them more environmentally sustainable. To empirically ground its argument, the article draws on two Toronto region case studies illustrating processes assuring the persistence of the durable features of North American suburbs: the layout of large suburban multifunctional centres and the themes raised by Rob Ford during his successful 2010 mayoralty electoral campaign.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".