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Record W2944206410 · doi:10.1177/1468796819847750

Visible minorities in the Canadian Labour Market: Disentangling the effect of religion and ethnicity

2019· article· en· W2944206410 on OpenAlexaboutno aff
Nabil Khattab, Sami H. Miaari, Marwan Mohamed-Ali

Bibliographic record

VenueEthnicities · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupIslamophobiaSocioeconomic statusVisibilityDemographic economicsInequalityDistribution (mathematics)SociologyIslamHierarchyImmigrationDiversity (politics)Political scienceGender studiesEconomicsGeographyDemographyPopulationLaw

Abstract

fetched live from OpenAlex

Studies on labour-market disadvantages of ethnic and visible minorities in Canada have focused, primarily, on earning differentials leaving other important socioeconomic indicators such as employment and occupational distribution insufficiently examined. These studies have rarely included religion as one of the explanatory variables, despite the presence of sizable religious communities and considerable religious diversity in Canada. Given the rise in anti-Muslim sentiment and the increase in Islamophobia, religion becomes an important factor. In this study, we argue that the Canadian labour market excludes/includes individuals based on their physical visibility and religious affiliation. We analyse data obtained from the Canadian 2011 National Household Survey. The analysis supports the existence of a hierarchy of labour market outcomes predicated on both visibility and religious affiliation. It is suggested that the existing labour market inequality among the various ethno-religious groups is shaped largely by physical visibility and cultural proximity to the dominant group. The results provide evidence for a ‘Muslim penalty’.

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)0.004
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 score0.021
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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