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Record W2495094032 · doi:10.7592/ejhr2016.4.2.polimeni

Jokes optimise social norms, laughter synchronises social attitudes: an evolutionary hypothesis on the origins of humour

2015· article· en· W2495094032 on OpenAlexaff
Joseph Polimeni

Bibliographic record

VenueEuropean Journal of Humour Research · 2015
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsJokeLaughterPsychologyJudgementCognitionSocial psychologyInferenceCognitive psychologyEpistemologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

A prominent humour theory suggests that most jokes will violate a subjective moral principle. This paper explores the ramifications of Thomas Veatch’s social violations theory of humour, and hypothesizes that jokes tend to produce four distinct humour emotions, in a sequential manner. The final emotional response to a humorous stimulus involves an aesthetic judgement about the inference of the joke. Humour could therefore be a cognitive-emotional mechanism used to appraise social norms while laughter serves to signal appreciation for the social inferences associated with the joke. It is further proposed that the cognitive-emotional structure of humour implies an evolutionarily adaptive function.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.404
GPT teacher head0.464
Teacher spread0.061 · 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.

Study designNot applicable
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

Citations2
Published2015
Admission routes1
Has abstractyes

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