A preliminary analysis of population and employment dynamics within the Greater Toronto Area, the Greater Montreal Area, and the Greater Vancouver Area between 2006 and 2016 in relation to customized accessibility geography: On the topics of the determinants of residential choice, gentrification, and sustainable urban growth
Bibliographic record
Abstract
"Metropolitan areas are the epicentre of urban growth in the 21st Century. According to U.NHabitat, over one-third of the world’s population and two-thirds of the world’s urban population now live and work in metropolitan areas. These ratios are predicted to expand at a rapid rate in the coming decades. Therefore, obtaining an in-depth understanding about the spatial structure of metropolitan areas, the internal distributions of population and economic activities, the factors that drive population and economic growth and their distributions, and the challenges they face in relation to managing growth is imperative to promote urban sustainability. This present research investigates these topics in the context of the three largest Canadian metropolitan regions: the Greater Toronto Area, the Greater Montreal Area, and the Greater Vancouver Area. Methodologically, using Statistics Canada’s proximity measure database, we construct an aggregated accessibility geography for each metropolitan region based on a clustering model. On top of this geography, we overlay the 2006 and 2016 Labour Force Survey data at the place of residence and place of work to examine the location of different types of workers and employments as well as spatial dynamics during the decade. The outcomes of our analyses contribute to the following knowledge from the Canadian experience: 1) determinants of locational choice, especially for members of the creative class; 2) gentrification and displacement; 3) sustainable urban growth in relation to the 15-Minute City. "@eng
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".