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Record W4285315405 · doi:10.26443/jcreor.v3i1.60

"Uyghurs in the Diaspora in Canada" 2021 Survey Report

2021· article· en· W4285315405 on OpenAlexafffundvenueabout
Susan J. Palmer, Marie-Ève Melanson, Dilmurat Mahmut, Abdulmuqtedir Udun

Bibliographic record

VenueJournal of the Council for Research on Religion · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsDiasporaHomelandChinaImmigrationGender studiesOppressionPolitical scienceEthnologySociologyMedia studiesLawPolitics

Abstract

fetched live from OpenAlex

This report presents the results of “The Uyghurs in the Diaspora in Canada” survey. It was conducted between November 2020 and January 2021, by the research team affiliated with the project, Children in Sectarian Religions and State Control at the School of Religious Studies, McGill University.[1] Our aim was to gather information on the Uyghurs who left their Homeland (East Turkestan/Xinjiang) and relocated to Canada. The survey consists of 45 questions that focus on why and how these immigrants came to Canada and what challenges they faced in China. Our respondents numbered 106, and our findings indicate that they were subject to widespread discrimination and oppression in China before emigrating to Canada. Other questions explore their contact with relatives in their Homeland and their level of religiosity since arriving in Canada. Finally, we sought to understand how they are currently attempting to preserve their Uyghur culture and language while living in diaspora. [1]. For further information on the project please visit the following site: Spiritual Childhoods – Children in Minority Religions, http://www.spiritualchildhoods.ca

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.349
GPT teacher head0.439
Teacher spread0.090 · 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

Citations5
Published2021
Admission routes4
Has abstractyes

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