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Record W4362453780 · doi:10.1177/08969205231163896

Critical Han Studies Through the Lens of Internal Colonialism: China, Guangdong, and Hong Kong

2023· article· en· W4362453780 on OpenAlexaff
David Chen, Jason A. Miller, Mark Shakespear

Bibliographic record

VenueCritical Sociology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHong Kong and Taiwan Politics
Canadian institutionsUniversity of British ColumbiaUniversity of VictoriaUniversity of Toronto
Fundersnot available
KeywordsChinaColonialismEthnic groupIdeologyPoliticsMandarin ChineseHierarchySubordination (linguistics)Political scienceHan chineseGender studiesGeographySociologyLinguisticsLaw

Abstract

fetched live from OpenAlex

When the concept of ‘internal colonialism’ has been applied to China, it has often been focused on the plight of ethnic minorities. The political and cultural subordination of non-Mandarin Han groups, however, has drawn little attention. We argue that critical Han studies, by posing a challenge to the state ideology of Han ethnic unitarism, provides a theoretical arsenal capable of broadening the application of the internal colonialism framework to the study of non-Mandarin Han groups and regions in China. To provide empirical support for our argument, we examine ethno-geographic representation among Chinese political elites. We find an internal heterogeneity and ethnic hierarchy between different Han groups who have integrated into the political ruling class of China, which is dominated by the Mandarins, to various extents: the Wu people of Shanghai and Zhejiang represent the top layer of the hierarchy; the Xiang of Hunan, the Hokkien of Fujian, and the Gan of Jiangxi constitute the intermediate layer; and the Cantonese and the Teochew of Guangdong belong to the bottom layer. These findings provide the basis for our discussion of internal colonization in China with a specific focus on Guangdong and Hong Kong.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.013
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.095
GPT teacher head0.442
Teacher spread0.347 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations4
Published2023
Admission routes1
Has abstractyes

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