Immigrant Settlement in Ontario: Location and Local Labour Markets
Bibliographic record
Abstract
ABSTRACT/RESUME Most immigrants to Canada settle in large gateway cities; few settle in smaller cities or rural areas. This article examines the spatial settlement patterns of immigrants throughout Ontario, devoting particular attention to smaller cities and rural areas. Our study triangulates various methods of analysis. First, a geographical analysis of census data maps the spatial settlement distribution of immigrants. Second, a multivariate statistical analysis investigates the link between settlement location, labour market, and housing characteristics. Finally, an interview survey complements the quantitative analysis and investigates the motivations and decision factors of the location choices of immigrants in areas outside of Toronto, the main immigrant gateway. La plupart des immigrants au Canada s'installent dans les grandes villes portails alors que tres peu s'installent dans des villes plus modestes ou en zone rurale. Cet article examine les modeles d'implantation des immigrants en Ontario, en portant une attention particuliere aux petites villes et aux zones rurales. Notre etude regroupe differentes methodes d'analyse. Tout d'abord, une analyse geographique des donnees du recensement permet d'etablir la cartographie de la repartition spatiale des immigrants. Ensuite, une analyse statistique multidimensionnelle demontre le lien entre le lieu d'implantation, le marche du travail et les caracteristiques de logement. Enfin, une enquete par interview complete l'analyse quantitative en se penchant sur les motivations et les facteurs decisionnels qui influencent le choix du lieu d'implantation des immigrants dans les zones situees a l'exterieur de Toronto, le point d'entree principal de l'immigration. INTRODUCTION In Canada, the vast majority of immigrants settle in urban regions, mainly the provinces of Ontario, Quebec, and British Columbia (Beshiri and Alfred 2002). Few immigrants settle in rural areas or in the Prairie or Atlantic provinces. This geographical imbalance has fostered a renewed interest among policy makers regarding how immigrant flows can be directed toward smaller cities and towns. Recent academic literature has also called for further investigation regarding the geographic distribution of immigrants in non-metropolitan regions (Beshiri and Alfred 2002; Citizenship and Immigration 2001; Hiebert 2000). Existing Canadian studies on immigrant settlement emphasize urban immigration, particularly the gateway cities of Toronto, Vancouver, and Montreal (Bauder and Sharpe 2002; Bourne 1989; Lo and Wang 1997; Ray 1999). What is missing from these studies is an assessment of immigrant settlement patterns across rural and urban regions, and an investigation of how these patterns relate to local labour market characteristics. Some researchers have explored immigrant settlement patterns in relation to labour market characteristics. For example, Abu-Laban et al. (1999) found that over half (54%) of the refugee respondents interviewed in Alberta cited lack of employment and educational opportunities as their motives for migrating to other cities. Only 14 percent of the participants identified proximity to family members, friends, or ethnic affiliation as major determinants of migration. Other research on immigrant settlement decisions has also singled out family, friends, and ethnic affiliations as influences on immigrant settlement (Kaplan 1995; McDonald 2004; Trovato 1988). Our paper complements this existing research. The results from our study are relevant to developing immigrant settlement policies and incentives in small cities, towns, and rural communities that seek to benefit from immigrants' skills and labour power. A review of the literature on immigrant settlement in North America comprises the first portion of the paper. The second explains our methods, involving geographic and multivariate analyses of census data and interviews. In the third section, our findings regarding the spatial distribution of immigrants across urban, intermediate, and rural regions in Ontario are presented, and we relate these patterns to selected labour market and housing characteristics. …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".