Planning for Intensifying Suburbs: Analyzing Markham and Vaughan
Bibliographic record
Abstract
In the North American context suburbs are where the majority of the population resides, as they attract families of all types, provide a variety of housing typologies as well as play critical roles in the economy and fixation of local governments. Though this development trend has been in strong demand for years, cities have become increasingly aware of the negative costs associated with sprawl, which has lead the government of Ontario to adopt smart growth principles. Since this time the government has made significant steps in order to curb sprawl, through the Places to Grow Act as well as the Greenbelt Act, where large masses of land and protected and growth is designated to certain highlighted growth centres. Both Markham Centre as well as the Vaughan Metropolitan Centre are part of Ontario’s growth centres as outlined in the Places to Grow Act. Analyzing literature on suburban intensification as well as plans and policies which have lead to the development of Markham Centre, this paper attempts to answer what the Vaughan Metropolitan Centre will become. The City of Vaughan is primarily a place of low density while also being automobile reliant, therefore the Vaughan Metropolitan Centre represents something completely different than the current landscape and does not belong to an existing area or neighbourhood. Using literature on suburban intensification as well as Markham Centre as an example of having good planning principles, the specific question put toward the Vaughan Metropolitan Centre in this Major Paper will ask if Vaughan’s downtown can be regarded as suitable and appropriate growth?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".