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Record W2780674921 · doi:10.1080/1369183x.2020.1724412

The resettlement of Vietnamese refugees across Canada over three decades

2020· article· en· W2780674921 on OpenAlexaffabout
Feng Hou

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsStatistics CanadaWestern University
Fundersnot available
KeywordsRefugeeVietnameseResidenceImmigrationSocioeconomic statusDemographic economicsEarningsCensusHuman capitalEducational attainmentPolitical scienceEthnic groupGeographyEconomic growthDemographyPopulationSociologyEconomics

Abstract

fetched live from OpenAlex

Welcoming 60,000 Southeast Asian refugees in the 1979–80 period has become a celebrated part of Canada's history, but the eventual integration of these refugees into Canadian society has received insufficient attention. This study provides a comprehensive overview of Vietnamese refugees’ economic outcomes over the three decades after their arrival. This study also explores how regional contexts contributed to shaping economic outcomes. Based on analyses of multi-year census data, this study finds that adult Vietnamese refugees arrived with little human capital, but they had high employment rates, and over time they closed their initial large earnings gap with other immigrants. Childhood Vietnamese refugees out-performed other childhood immigrants and similar-aged Canadian-born individuals in educational attainment and earnings when they reached adulthood. The geographic region of residence was associated with some large variations in refugees’ socioeconomic outcomes; and regional differences in refugees’ human capital characteristics, ethnic enclave, and economic conditions played varying roles depending on the outcome measure and length of residence.

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)0.001
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 score0.019
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
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 head0.068
GPT teacher head0.411
Teacher spread0.343 · 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

Citations21
Published2020
Admission routes2
Has abstractyes

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