Family residential choices in Montreal metropolitan area: A community-based analysis
Bibliographic record
Abstract
Whether families with children choose to live in the suburbs to benefit from cheap land or do so to avoid decaying socio-economic conditions of central neighbourhoods remains a central question for public policy. To explore that question, this article investigates the characteristics of communities within the Montreal metropolitan area where families with children have chosen to live (108 communities). Three different analyses are undertaken. The first analysis is a community-based estimation of the Alonso model. It shows that the average price of a single-family house within a community varies with distance to the Central Business District (CBD), and that space per household for housing grows in relation with distance. The second analysis is inspired by life-cycle arguments. It shows that housing space is not as significant as new housing development or poverty to explain the proportion of families with children in communities of the metropolitan area. The third analysis, using data pertaining to families with children only, shows that communities closer to the CBD have a higher proportion of low-income families as well as high-income ones. These communities also have a higher proportion of families with parents that are immigrants, have university degrees, are single (divorced), and are under 35 years old. Mothers are also more likely to be active in these communities. We conclude that developing homogenous neighbourhoods designed for upper-middle-class households and knowledge-based workers may better contribute to the attraction of families with children in Montreal’s central neighbourhoods than building affordable housing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".