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Record W2740211514 · doi:10.1515/humor-2016-0087

Punches or punchlines? Honor, face, and dignity cultures encourage different reactions to provocation

2017· article· en· W2740211514 on OpenAlexaffabout
Kuba Kryś, Cai Xing, John M. Zelenski, Colin A. Capaldi, Zhongxin Lin, Bogdan Wojciszke

Bibliographic record

VenueHumor - International Journal of Humor Research · 2017
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsCarleton University
Fundersnot available
KeywordsDignityHonorProvocation testContext (archaeology)AggressionPsychologyAmusementFace (sociological concept)Social psychologySociologyLawMedicineHistoryPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Abstract Research on culture-related violence has typically focused on honor cultures and their justification of certain forms of aggression as reactions to provocation. In contrast, amusement and humor as the preferred reactions to provocation remain understudied phenomena, especially in a cross-cultural context. In an attempt to remedy this, participants from an honor culture (Poland), dignity culture (Canada), and face culture (China) were asked how they would react and how they would like to react to a series of provocative scenarios. Results confirmed that aggression may be the preferred reaction to provocation in honor cultures, while the preferred reaction to provocation in dignity cultures may be based on humor and amusement. The third kind of provocation reaction, withdrawal, turned out to be more complex but was most popular in dignity and face cultures. Furthermore, results confirmed that the way individuals think they would behave is more culturally diversified than the way individuals would like to behave.

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)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.289
GPT teacher head0.546
Teacher spread0.258 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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