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Record W2030610505 · doi:10.1068/a37246

The Migration–Immigration Link in Canada's Gateway Cities: A Comparative Study of Toronto, Montreal, and Vancouver

2006· article· en· W2030610505 on OpenAlexaffabout
Feng Hou, Larry S. Bourne

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of TorontoStatistics Canada
Fundersnot available
KeywordsImmigrationMicrodata (statistics)Net migration rateMetropolitan areaDemographic economicsGeographyInternal migrationPopulationRestructuringGateway (web page)DemographyEconomic geographyPolitical sciencePopulation growthCensusSociologyEconomics

Abstract

fetched live from OpenAlex

In this paper we explore the links between internal migration and international immigration in Canada's three largest metropolitan areas. In particular, we use a place-specific approach to test the displacement hypothesis that the migration behaviour of the less-well-educated native-born population is sensitive to the inflows of immigrants. Based on analyses of microdata from five consecutive censuses covering the period from 1981 to 2001, we find that the migration–immigration relationships are complex, often subtle, and inconsistent across the three cities. Growth in the immigrant population is correlated with an increased out-migration rate among the less-well-educated native-born population, but only in Toronto and Vancouver. This correlation, however, is not independent of changes in housing prices. We also find no consistent support for an alternative hypothesis that economic restructuring accounts for the net out-migration from immigrant gateway cities.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2006
Admission routes2
Has abstractyes

Explore more

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