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Record W164737559

Immigrant Settlement in Ontario: Location and Local Labour Markets

2005· article· en· W164737559 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian ethnic studies · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationGeographyHumanitiesSettlement (finance)EthnologyPolitical scienceSociologyArchaeologyBusiness
DOInot available

Abstract

fetched live from OpenAlex

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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.324
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it