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Record W2035886591 · doi:10.5539/ass.v8n7p3

Online Cultural Conservatism and Han Ethnicism in China

2012· article· en· W2035886591 on OpenAlexvenueno aff
Matthew Ming-tak Chew, Wang Yi

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsChinaIdeologyConservatismEthnic groupModernityPoliticsSociologyChinese cultureRealismPolitical scienceEpistemologyAnthropologyLawPhilosophy

Abstract

fetched live from OpenAlex

This research note analyzes the intellectual and ideological contents of cultural conservative discourses on the internet in the past several years in China. Our findings show that online cultural conservatives valorize Han ethnic culture and Chinese cultural tradition at the same time through conflating the two. They also demonstrate that online cultural conservatives reinterpret historical China in order to represent it in a totally positive light and that they vehemently attack the negative images of historical China found in intellectual, official, and popular cultural discourses. They claim that the real culprit that had prevented historical China from progressing into modernity was a non-Han ethnic group (the Manchus). Our analysis on political thoughts of online cultural conservatives shows that they partially agree with Chinese neo-leftists and liberals on critical assessment of contemporary Chinese reality but they diverge greatly from the two schools on the choice of solution for the problems. Online cultural conservatives’ proposal is to reinvigorate traditional Chinese culture and their political vision combines international relations realism, cultural determinism, and Han ethnicism.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.347
Teacher spread0.305 · 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 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

Citations9
Published2012
Admission routes1
Has abstractyes

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