<scp>M</scp>igration, commuting distance, and urban sustainability in Ontario's Greater Golden Horseshoe: Implications of the<i>Greenbelt</i>and<i>Places to Grow</i>legislation
Bibliographic record
Abstract
Southern Ontario's Greater Golden Horseshoe (GGH) is the most heavily populated and urbanized region in Canada. Given its large population size, economic importance, and projected population growth, the Ontario provincial government recognized the need to plan for the growth of jobs and people to avoid the adverse effects of urban sprawl, traffic gridlock, and the loss of farmland and natural areas. Ontario's 2005Greenbelt Plan and 2006 Growth Plan for the Greater Golden Horseshoe (Places to Grow)established the legislative framework to guide development and population growth within southern Ontario. While directing development and promoting population growth in specified areas, an unintended consequence of these legislative plans may, however, be increased commuting distance as workers commute from beyond the Greenbelt into the employment dense areas inside the Greenbelt. This article focuses upon the intersection between migration and commuting distance in southern Ontario's GGH region, within the context of ongoing population growth, and Greenbelt and Places to Grow legislation. Results indicate that migrants moving beyond the Greenbelt have generally longer commute distances, with implications for the sustainability of communities and government policies aimed at reducing the carbon footprint.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".