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Record W4380422302 · doi:10.1111/cars.12442

Intersections on the road to skills’ transferability: The role of international training, gender, and visible minority status in shaping immigrant engineers’ career attainment in Canada

2023· article· en· W4380422302 on OpenAlexaffabout
Alla Konnikov

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDisadvantageImmigrationTransferabilityEthnic groupPolitical scienceCensusField (mathematics)SociologyGender studiesEconomic growthDemographyLawPopulation

Abstract

fetched live from OpenAlex

This paper focuses on the engineering profession in Canada, a regulated field with a large proportion of internationally trained professionals. Using Canadian census data, this study addresses two main questions. First, I ask whether immigrant engineers who were trained abroad are at increased disadvantage in gaining access (1) to employment in general, (2) to the engineering field, and (3) to professional and managerial employment within the field. Second, I ask how immigration status and the origin of training intersect with gender and visible minority status to shape immigrant engineers' occupational outcomes. The results reveal that immigrant engineers who were trained abroad are at increased risk of occupational mismatch and this risk is two-fold and intersectional. First, they are at a disadvantage to enter the engineering field. Second, those employed in the engineering field are more likely to occupy technical positions. These forms of disadvantage intensify and diversify for women and racial/ethnic minority immigrants. The paper concludes with a discussion of immigrants' skills transferability in regulated fields from an intersectional perspective.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.080
GPT teacher head0.307
Teacher spread0.228 · 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 teacher head, 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

Citations6
Published2023
Admission routes2
Has abstractyes

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