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Record W4297514174 · doi:10.3138/cpp.2022-007

Employer Attitudes and the Hiring of Immigrants and International Students: Evidence from a Survey of Employers in Atlantic Canada

2022· article· en· W4297514174 on OpenAlexaffvenueabout
Tony Fang, Na Xiao, Jane G. Zhu, John Hartley

Bibliographic record

VenueCanadian Public Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsLaurentian UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsImmigrationProbit modelOrdered probitMulticulturalismDemographic economicsPerceptionProbitSample (material)PsychologySocial psychologyPolitical scienceEconomicsPedagogyLaw

Abstract

fetched live from OpenAlex

What are employers’ perceptions regarding hiring immigrants and international students in Atlantic Canada? How are these perceptions related to hiring outcomes? Our analysis, based on a 2019 random representative survey of 801 employers, finds that most report positive attitudes toward immigrants and international students. Probit analysis of the sample of employers who report receiving applications from immigrants and international students also finds that hiring from this group is positively associated with employers’ belief that multiculturalism enhances creativity in the workplace and (less clearly) with the belief that immigrants and international students are harder working than native-born local workers; negatively with beliefs that such workers accept lower pay, have language barriers, have higher training costs, hold unreliable credentials, and (less strongly) have lower retention probabilities; and not consistently with the belief that such workers may help in increasing exports, are unfamiliar with the Canadian culture or workplace, or may take jobs away from locals.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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

Citations13
Published2022
Admission routes3
Has abstractyes

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