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Record W3179260652 · doi:10.1080/13602004.2021.1947586

Belonging to Quebec and English Canada as Muslims: The Perspectives of the Highly Educated Uyghur Immigrants

2021· article· en· W3179260652 on OpenAlexfundaboutno aff
Dilmurat Mahmut

Bibliographic record

VenueJournal of Muslim Minority Affairs · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsnot available
FundersFonds de Recherche du Québec-Société et Culture
KeywordsGender studiesIslamophobiaDiasporaImmigrationIdentity (music)White (mutation)NarrativePoliticsSociologyConsciousnessRacismIntersectionalityPolitical scienceLawPsychology

Abstract

fetched live from OpenAlex

Following the rise of Islamophobia, Muslims in the West have been experiencing increasingly challenging identity dilemmas. Canada is not an exception. This article, at the intersection of Critical Race Theory and post-colonial perspectives, analyzes the narratives of 13 highly educated Uyghur Muslim immigrants living in Quebec and some English provinces of Canada. Their stories show that many of them have become subject to multiple identity dilemmas common to other Muslim diaspora groups, while also facing some challenges unique to their own background. This article further highlights the Uyghur’s experiences through a new angle: they all appear to have developed an us/Muslim immigrant vs. them/white Canadians’ dichotomy. In the province of Quebec, their narratives reveal “oppositional consciousness” against the dominant white Quebecers, which is quite political, while in English provinces they may see their Muslim identity more as “oppositional culture” against the white English Canadians, which is much less political.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0370.014
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.244
Teacher spread0.237 · 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 designQualitative
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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Muslim Minority AffairsSame topicChina's Ethnic Minorities and RelationsFrench-language works237,207