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Record W2082271810 · doi:10.1080/21632324.2014.915495

Effects of immigration on interregional population flows in Canada

2014· article· en· W2082271810 on OpenAlexafffundabout
Yigit Aydede

Bibliographic record

VenueMigration and Development · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsSaint Mary's University
FundersUniversity of Windsor
KeywordsImmigrationMetropolitan areaCensusDemographic economicsEconomicsPopulationImmigration policyArgument (complex analysis)Labour economicsGeographyDemographySociology

Abstract

fetched live from OpenAlex

The purpose of this article is to investigate one possible mechanism by which the Canadian labour markets adjust to immigration. Despite the fact that Canada is one of the major immigrant receiving countries in the world, most studies that look across Canadian local markets have found immigration’s effects to be weak. The well-known argument is that rising immigration levels in an area may result in the out-migration of an area’s residents if the immigrants displace the local workers in employment, bid down wages, or cause housing prices to rise through increased demand for shelter. The present study investigates this bias by estimating the mobility responses of local residents to immigrant inflows based on a spatial equilibrium model for 28 census metropolitan areas (CMA) from 2000 to 2009. The results show that immigration has a significant displacement effect between CMAs.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.027
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.227
Teacher spread0.221 · 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

Citations1
Published2014
Admission routes3
Has abstractyes

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