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Record W2142675136 · doi:10.19030/iber.v9i6.584

Predicting The Occupational Choices Of Foreign-Born And Second-Generation Canadians: Evidence From Census Data

2010· article· en· W2142675136 on OpenAlexaboutno aff
Najma R. Sharif

Bibliographic record

VenueInternational Business & Economics Research Journal (IBER) · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationMultinomial logistic regressionResidenceCensusDemographic economicsContext (archaeology)Foreign bornHuman capitalOccupational prestigeDemographyEconomicsGeographySociologySocioeconomic statusPopulationStatisticsEconomic growthMathematics

Abstract

fetched live from OpenAlex

In this paper, we study the occupational mobility of immigrants and the intergenerational transfer of their occupational status in Canada. The former is done by predicting their occupational choices and examining shifts in those choices as the length of residence in Canada increases, while the latter is studied by comparing the choices of immigrants with those of second-generation Canadians. Of special interest is the role that gender plays in this regard. We use the Schmidt-Strauss (1975) multinomial logit model to explain occupational choice in terms of human capital & other characteristics with additional controls for immigration status, and the length of residence in Canada. This model is estimated using data from the public-use micro-data files from the 2001 Canadian Censuses, and then used in a simulation context to predict the occupational profiles of all native-born, foreign-born and second-generation Canadian men and women in the aggregate, and also of various cohorts of immigrants.

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)0.006
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.152
GPT teacher head0.408
Teacher spread0.256 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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