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Record W4210652321 · doi:10.1080/10357823.2022.2025576

Hiding in the Shadows: Resistance and Antagonism of Everyday Minority Nationalism in China

2022· article· en· W4210652321 on OpenAlexaff
Yao Qu

Bibliographic record

VenueAsian Studies Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNationalismEthnic groupChinaGender studiesEveryday lifeContext (archaeology)Ethnic nationalismSociologyIdentity (music)Political sciencePoliticsAnthropologyLawHistoryAesthetics

Abstract

fetched live from OpenAlex

Since 1949, ethnic minority nationalism in Xinjiang Uyghur Autonomous Region (XUAR) has consistently concerned the People’s Republic of China. Most previous studies on nationalism in XUAR focused on organised visible nationalist movements and ignored everyday life, particularly regarding interactions between the government and citizens or between the majority and the minority. This study investigates daily practices of ethnic minority nationalism in XUAR within the framework of everyday nationalism. It explores daily life in Altay City to determine how members of ethnic minority groups produce and maintain their sense of ethnic identity in this context. The article found that members of ethnic minority groups are engaged in a struggle to retain their sense of ethnic identity by resisting state nationalism and expressing antagonism towards Han Chinese in their everyday lives. This study reveals a covert nationalist movement involving almost everyone in XUAR. Ethnic nationalism is central to XUAR society and will continue to shape its social relations.

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)0.001
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 score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.362
Teacher spread0.316 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueAsian Studies ReviewSame topicChina's Ethnic Minorities and RelationsFrench-language works237,207