<scp>D</scp>etermining factors of residential migration from the central city to the suburbs in the metropolitan region of Montreal: The linguistic divide and flight of the French‐speaking population
Bibliographic record
Abstract
Abstract The purpose of this study is to gain a greater understanding of residential migration within the metropolitan region of Montreal by examining what factors determine flows between the central city and the suburbs. Using a life‐cycle perspective, a logistic regression model was developed. The results call attention to the critical factors that shape migration patterns from the central city to the suburbs: living in a two‐person household, with or without children; being 20 to 39 years of age; speaking French at home; being employed; and not being poor. The results also showed that belonging to a visible minority is not a significant factor in favour of migrating to the suburbs. However, since studies have found that the odds of Francophones leaving Montreal for the suburbs are much greater than for Anglophones or allophones, even after controlling for the other characteristics, residential mobility seems to be associated with spatial segregation based on language, rather than membership in an ethnic group. If “White flight” does not really exist in Montreal, a similar phenomenon with respect to Francophones is taking place which could be labelled “French flight.”
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 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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.016 | 0.001 |
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".