Contextualizing the Growth Plan: The Intersection of Regional Growth Management Planning and Smart Growth in a Suburban Region
Bibliographic record
Abstract
The paper explores how regional growth management planning legislation and policies are rolled out on the ground at different levels of government in Ontario using the Region of Peel as a scenario. Further examined are the challenges associated with suburban sprawl and Ontario’s response to addressing these challenges through the concept of smart growth to create complete communities, which became the underlying ideology of Ontario’s first regional growth management policy framework The Growth Plan for the Greater Golden Horseshoe, 2006. A review of Ontario’s top-down planning system is undertaken to understand how municipalities make planning decisions to address the location and density of growth from the Province down to municipalities. The Region of Peel is reviewed along with the Official Plans of its lower-tier municipalities as a means of examining how upper-tier municipalities assist in coordinating growth amongst their lower-tier municipalities. Through first person interviews and secondary research, it uncovered that the Region of Peel has a limited role in the development process. My review indicated two potential explanations for the Region’s limited role in the development review process which has affected its ability to enforce characteristics of complete communities in new developments. To help facilitate and encourage the development of complete communities through the development process, the Region of Peel implemented the Healthy Development Assessment (HDA) which provides recommendations during the development application process to create developments that are pedestrian-friendly, transit-supportive and have a mix of uses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".