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Record W3003309740 · doi:10.1002/psp.2316

Are refugees more likely to leave initial destinations than economic immigrants? Recent evidence from Canadian longitudinal administrative data

2020· article· en· W3003309740 on OpenAlexafffundabout
Lisa Kaida, Feng Hou, Max Stick

Bibliographic record

VenuePopulation Space and Place · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsStatistics CanadaMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsImmigrationRefugeeResidenceDemographic economicsDestinationsSettlement (finance)GeographyImmigration policyPolitical scienceDemographyEconomic growthSociologyBusinessEconomicsPayment

Abstract

fetched live from OpenAlex

Abstract Secondary migration is of policy interest in many immigrant‐receiving countries when efforts are made to steer immigrants away from major urban centres. One example is refugee dispersal policy. Although previous research, mostly evaluating the policy itself, argues it would disproportionately increase the secondary migration of refugees settled in nongateway cities, quantitative analysis is limited. This study compares the long‐term secondary migration in Canada by immigrant admission category with a focus on the city size of initial settlement. Our analysis of the Longitudinal Immigration Database finds that although resettled refugees have a higher overall secondary migration rate than economic immigrants, their difference in the likelihood of leaving a same‐size initial destination city is minor. The majority stay in the initial city of residence regardless of admission category. The findings have a strong policy implication; the geographic distribution of immigrants can be influenced most effectively at arrival.

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)0.002
metaresearch head score (Gemma)0.014
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 score0.024
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.182
GPT teacher head0.408
Teacher spread0.226 · 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

Citations22
Published2020
Admission routes3
Has abstractyes

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