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Record W4206705496 · doi:10.1177/19485506211065938

The Impact of Culture and Social Distance on Humor Appreciation, Sharing, and Production

2021· article· en· W4206705496 on OpenAlexafffund
Yi Cao, Yubo Hou, Zhiwen Dong, Li‐Jun Ji

Bibliographic record

VenueSocial Psychological and Personality Science · 2021
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsJokeConstrual level theoryPsychologySocial psychologySocial distancePerspective (graphical)Independence (probability theory)Linguistics

Abstract

fetched live from OpenAlex

Building on the benign violation theory and self-construal theory, we conducted four studies to examine how culture and social distance would influence humor appreciation, sharing, and production. Study 1 found that Chinese participants appreciated and intended to share a joke involving distant others more than that involving close others. They also generated funnier titles for a joke involving distant others than close others. Studies 2a and 2b compared Chinese and Americans using various types of jokes, replicating the social distance effect among Chinese but finding little effect of social distance among Americans. In Study 3, interdependence-primed participants generated more humorous titles for a joke involving distant than close others, whereas independence-primed participants showed no effect of social distance. The research provides further support to the benign violation theory from a cultural perspective and has important implications for cross-cultural communications.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
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 head0.077
GPT teacher head0.437
Teacher spread0.360 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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