Bibliographic record
Abstract
This chapter considers how immigration influences the contemporary evolution of Canadian cities. 97 per cent of immigrants arriving between 2001 and 2006 and 95 per cent of Canada's total foreign-born population live in urban areas. Given these numbers, it is not surprising that immigration is a major source of social transformation in cities across the country. But immigrants do not randomly distribute themselves across Canada's urban system. Complex and dynamic constellations of variables determine how immigrants settle into particular places and what will be the consequences of this settlement for cities and residents, both immigrant and native-born. Immigration is a long-term, complex process. Factors at all geographic scales - global, national, regional, and local - influence the origins, flows, and destinations of immigrants. The socio-spatial patterns that emerge at different points along migrants' journeys often extend across generations. We have organized this chapter along the three main stages of the immigrant's journey: arrival, settlement, and integration. For each stage, we explore the experiences of immigrants and their contributions to both continuity and change in Canadian cities. We conclude with a spatial overview of the Canadian geography of immigration presented as the culmination of these factors and interacting dynamics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".