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Record W1898560325 · doi:10.25336/p6x60f

Skilled immigrant labour: country of origin and the occupational locations of male engineers

2002· article· en· W1898560325 on OpenAlexaffvenueabout
Mónica Boyd, Derrick Thomas

Bibliographic record

VenueCanadian Studies in Population · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsStatistics CanadaUniversity of Toronto
Fundersnot available
KeywordsImmigrationCensusHuman capitalMultinomial logistic regressionDemographic economicsForeign bornAccreditationCountry of originGeographyDemographyStock (firearms)SociologyPolitical scienceEconomicsPopulationEconomic growth

Abstract

fetched live from OpenAlex

Do high skill immigrant workers find employment corresponding to their training? Using unpublished data from the 1996 census, we examine the occupational locations of men age 30-54 who have a university degree with a major in engineering. We focus on three groups: Canadian born, foreign born who immigrated before age 19 and the foreign-born arriving after age 27, arguing that the first two are most likely to be educated in Canada whereas the last group is not. We find birth place differences in the percentages who are working in managerial, engineering, technical and all other occupations, with differences being most pronounced for those immigrating after age 27. Multinomial logit analysis confirms that these differences cannot be attributed to differences in measured human capital stock. Accreditation requirements are one likely explanation, particularly for those who have received training outside Canada.

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 score0.415
Threshold uncertainty score0.834

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.305
Teacher spread0.279 · 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

Citations72
Published2002
Admission routes3
Has abstractyes

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