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Record W2100424544 · doi:10.1177/0022022110361774

The Dynamics of Face Loss Following Interpersonal Harm for Chinese and Americans

2010· article· en· W2100424544 on OpenAlexaff
Yuan Liao, Michael Harris Bond

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInterpersonal communicationPsychologySocial psychologyHarmPersonalityNorm (philosophy)CredibilityFace (sociological concept)SociologyPolitical science

Abstract

fetched live from OpenAlex

Face concerns arise in any encounter where one’s credibility as a social actor is questioned by the flow of interpersonal exchanges. Being the target of another’s harmful acts raises the question of one’s deservingness for that harm, thereby bringing one’s face into question. The present study tests whether Hong Kong Chinese are more sensitive to the consequent loss of their own face than are Americans, when targeted during interpersonal encounters. It addresses this question by assessing whether two factors predicting face loss are universal or culturally specific in their impacts. The factors are loss of relative power following the harm doing and degree of norm violation characterizing the harm doing. The results showed that loss of relative power in the relationship by the target vis-à-vis the perpetrator predicted face loss for Americans, but not for Hong Kong Chinese. Although greater norm violation led to more face loss in both groups, the linkage between the two constructs was stronger for Hong Kong Chinese than for Americans. The culturally derived personality variables of horizontal-vertical individualism-collectivism were, however, unable to unpackage either cultural difference in linkage strength. Possible reasons for these findings are discussed, and suggestions are offered for increasing the scope of this basic model in explaining face loss following interpersonal harm.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.486
Teacher spread0.427 · 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.

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

Citations46
Published2010
Admission routes1
Has abstractyes

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