Short-Term Residential Changes to Toronto's Immigrant Communities: Evidence from Lsic Wave 1
Bibliographic record
Abstract
As Canada's biggest metropolitan area, Toronto has a large immigrant population and attracts a major proportion of the country's new arrivals. Although it is well known that new immigrant arrivals are highly mobile, there is limited understanding of how this mobility impacts settlement patterns, particularly in the period following arrival in Canada and at small spatial scales. Using Wave 1 of the Longitudinal Survey of Immigrants to Canada (LSIC) and a variety of spatial-analytical methods, this article examines the short-term evolution of Toronto's immigrant population over their first six months in Canada. The impacts of various individual and household characteristics are evaluated to determine reasons for mobility within the Toronto CMA, as well as in- and out-migration from the CMA. Results suggest that whereas mobility is high, new arrivals primarily remain in their initial destination with little difference in the overall distribution. Residential moves are associated with various individual and household characteristics, along with neighborhood effects and the type of housing initially occupied.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".