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Record W4235129827 · doi:10.32920/ryerson.14644911

Labour market experiences of Muslims pre and post 9/11

2021· preprint· en· W4235129827 on OpenAlexaffabout
Nida Kazmi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsImmigrationRacismIntersectionalityCredentialIslamophobiaQualitative researchIdentity (music)Gender studiesSociologyIslamDemographic economicsState (computer science)Political scienceEconomicsGeographyLawPoliticsSocial science

Abstract

fetched live from OpenAlex

This study offers an encompassing analysis of labour market experiences of Muslim immigrants in the GTA. A qualitative research method was used to explore whether the events of September 11 has impacted the labour market integration of Muslim immigrants in Canada. The findings are based on the responses of 6 first generation Muslim immigrants who were interviewed for this study. At first the participants reported that their identity as a Muslim did not play a significant role in the labour market, however, their stories suggest some level of racial discrimination as a result of their religious affiliation. The findings suggest intersectionality among Credential recognition, Canadian experience and racism that work together to veil racist activities that Muslim individuals encounter. This study highlights the state and accreditation institutions as key players that keep the immigrant out of the highly desired occupation to reserve these occupations for Canadian born and educated workers.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.700

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.004
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.074
GPT teacher head0.388
Teacher spread0.314 · 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
Published2021
Admission routes2
Has abstractyes

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