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Record W4313647705 · doi:10.1177/18681026221139301

Unpacking “the West”: Divergence and Asymmetry in Chinese Public Attitudes Towards Europe and the United States

2023· article· en· W4313647705 on OpenAlexaff
Adam Liu, Xiaojun Li, Songying Fang

Bibliographic record

VenueJournal of Current Chinese Affairs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of British Columbia
FundersUniversità BocconiNational University of Singapore
KeywordsChinaMainstreamPublic opinionAntipathyPolitical scienceDivergence (linguistics)RespondentPerceptionOpinion pollDevelopment economicsSociologyPoliticsPsychologyEconomicsLaw

Abstract

fetched live from OpenAlex

Recent public opinion polls conducted in Europe and the United States show increasingly negative views of China. Does the Chinese public hold similar views of “the West”? Conducting a two-wave survey in China, we found great divergence and asymmetries in Chinese public perceptions. First, Chinese views of European countries and the US diverge sharply, despite these countries being typically grouped together as “the West” in mainstream English and Chinese discourses; the Chinese viewed the US much more negatively than Europe. Second, whereas the Chinese reciprocated American antipathy, there was an asymmetry in public perceptions between China and Europe, with the Chinese expressing much greater favourability towards European countries than the other way around, though the degree of favourability still varied by country. Analyses of respondent attributes also yielded insights that both confirm and challenge some of the conventional wisdom regarding age, education, and party membership in Chinese public opinion.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
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 head0.054
GPT teacher head0.379
Teacher spread0.325 · 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

Citations22
Published2023
Admission routes1
Has abstractyes

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