Suburban growth in the Toronto CMA, 1996-2016: A Case of Johnny Town-Mouse and Timmy Willie
Bibliographic record
Abstract
This report addressed three questions: \n \n1.\tWhat proportion of Toronto residents live in suburbs, and what is their distribution? \n2.\tHow has this proportion and distribution changed over time? \n3.\tAre local growth management policies achieving their targets and objectives in Toronto? \n \nTo help answer these questions, proven methods to describe population distribution were employed using data from the Statistics Canada Census for 2016, 2006 and 1996. The results classified all 1,151 census tracts in the Toronto Census Metropolitan Area (CMA) as either active core, transit suburb, auto suburb, or exurb. \n \nActive cores and transit suburbs were generally considered to be locations of more sustainable development. In these locations, higher proportions of commuters walked, cycled, or used some form of transit. Auto suburbs and exurbs were generally considered to be locations of less sustainable development. In these locations, higher proportions of commuters drove personal vehicles and population densities were lower. \n \nPolicies from the Growth Plan for the Greater Golden Horseshoe emphasize intensification and compact development. Despite a slowed growth rate, auto suburbs account for the same proportion of CMA population in 2016 as they did in 1996. Their large volume makes for slow work to decrease their proportion. For every success where an auto suburb in 1996 became an active core (Newmarket Centre) or transit suburb (Etobicoke Centre or Scarborough Centre) for 2016, scores of other examples exist where greenfield lands in exurban areas were developed and became auto suburbs – from Milton, Oakville, and Brampton to Vaughan, Markham, and other municipalities. \n \nGrowth in transit suburbs was primarily experienced along major transit corridors in the inner suburbs of the City of Toronto while growth in active cores was primarily expressed as an expansion of the CMA core area. The stark lack of active cores, and even transit suburbs, outside the City of Toronto demonstrates an ineffectiveness of plan policies promoting suburban transit-oriented development to date. Perhaps a review of census data in 2021 will reveal improved results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".