A Statistical Approach for Analyzing Residential Isolation and its Determinants for Immigrant Communities: an Application to the Montréal Metropolitan Region
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Bibliographic record
Abstract
The aim of this paper is to measure the net propensity to live in isolation for Montréal’s main immigrant communities and to identify specific profiles that are particularly isolated. For that purpose, a statistical approach is used based on individual determinants to compute standardized isolation indexes that take into account the socioeconomic composition of the different groups. The models we developed also reveal how individuals’ characteristics, such as generational status, date of migration, education, language abilities or income, affect their residential isolation. Results reveal that many individual characteristics have strong impacts on residential isolation, and that those impacts are not always the same among immigrant communities. Also, the low propensity to live in isolation observed for all immigrant communities suggests that the place stratification model is probably not relevant to explain the residential dynamics of immigrant communities in Montréal. However, some vulnerable groups are much more likely to live in isolation: Haitian and South Asian with low education, low-income Maghrebis, and Filipinos who arrived via the Live-in Caregivers program. Some wealthy groups are also more isolated, such as Italians arrived before 1981. Therefore, considering this wide heterogeneity among immigrant communities, studies on their residential dynamic should not consider them as a whole.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it