Les trajectoires résidentielles des nouveaux immigrants à Montréal: Une analyse longitudinale et conjoncturelle
Bibliographic record
Abstract
Using the data from the longitudinal study L'Établissement des nouveaux immigrants (ÉNI), this paper analyses the residential trajectories of new immigrants in Montreal during the 1990's. Two methodologies were applied. First, survival models were used to identify the factors that determine the probabilities of moving and of access to homeownership. Second, the immigrants' mobility within the Montreal metropolitan area was analysed with conditional Logit models. The results show that high-skilled immigrants have a better situation on the housing market, and that there is a strong relation between employment and housing. We also notice a residential dispersion from downtown to the suburbs, especially in spaces where the houses are older and the average income lower. Apart from that, our results illustrate the process of residential concentration, for immigrants from the industrial countries, as well as for people from third-world countries. À l'aide des données de l'enquête sur l'Établissement des nouveaux immigrants (ÉNI), nous examinons les trajectoires résidentielles des nouveaux immigrants à Montréal pendant les années 1990. Nous utilisons d'abord les modèles de durée pour identifier les facteurs qui influencent les probabilités de déménager et de devenir propriétaire. Ensuite, nous analysons les déplacements des immigrants à l'intérieur de la RMR de Montréal en utilisant le modèle Logit conditionnel. Les résultats montrent que les immigrants plus qualifiés se trouvent dans une situation plus favorable sur le marché du logement et que la trajectoire résidentielle des immigrants est étroitement liée à leur emploi. Nous constatons une dispersion résidentielle du centre-ville vers les espaces périphériques dont le cadre bâti est plus ancien et le revenu moyen plus faible. De plus, nos résultats témoignent d'une concentration résidentielle tant chez les immigrants provenant des pays en développement que chez ceux provenant des pays développés.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".