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Inter-cultural interactions: the Chinese context

2012· book-chapter· en· W1680078501 on OpenAlexaff
David C. Thomas, Yuan Liao

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSituational ethicsSalience (neuroscience)Cultural psychologyIdentification (biology)PsychologySocial psychologyChinese culturePerceptionCultural diversityChinese peopleScripting languageSociologyCognitive psychologyChinaPolitical scienceAnthropologyComputer science

Abstract

fetched live from OpenAlex

Abstract Much of the written work on Chinese psychology identifies and describes culture-specific aspects of psychology as it relates to the Chinese people. This identification of cultural specifics (emics) is important, but it is only a first step towards understanding the interactions of Chinese people across cultures. This article discusses a number of intermediate mechanisms or conduits through which Chinese culture influences inter-cultural interactions. These mechanisms involve how Chinese people think about, evaluate, and respond to people who are culturally different. The article describes a behavioural sequence between culturally different actors that involves a number of conduits that are influenced by specific aspects of Chinese culture, such as the salience of situational cues, culturally based scripts and expectations, selective perceptions, out-group identification and attitudes toward out-groups, and the motivational influence of the self-concept. It concludes with a discussion of the implications of the review and discussion for research on Chinese psychology.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0070.009
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.081
GPT teacher head0.311
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations17
Published2012
Admission routes1
Has abstractyes

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