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Record W2966283194 · doi:10.7592/ejhr2019.7.2.brown

Individual differences in the way observers perceive humour styles

2019· article· en· W2966283194 on OpenAlexaff
Bruce Findlay, Jay K. Brinker

Bibliographic record

VenueEuropean Journal of Humour Research · 2019
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComedyPsychologyPerceptionStyle (visual arts)Social psychologyLaughterArtLiterature

Abstract

fetched live from OpenAlex

Humour has been conceptualized as styles, which vary based on their function (Martin, Puhlik-Doris, Larsen, Gray, and Weir, 2003). Research examining if and how observers perceive this intent is limited. The current study addresses this research gap by examining the perceptions of Martin et al.’s (2003) four humour styles. Additionally and of particular interest, was whether self-defeating humour and another self-directed humour style, self-deprecating humour, were perceived as two independent humour styles. Despite being similar in content, self-deprecating humour is associated with higher self-esteem and self-defeating humour with lower self-esteem. Two hundred and four students watched comedy clips and completed a survey online. Participants were asked to categorize each video clip by humour style and to rate the self-esteem of the target (i.e. comedian). Results revealed that humour styles are distinguishable by observers with participants predominantly selecting one humour style over the others for each clip. In support of the second hypothesis, targets who were categorised as using self-deprecating humour were perceived as having higher self-esteem than those categorised as using self-defeating humour, illustrating a distinction in the perception of these humour styles at an interpersonal level.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.228
GPT teacher head0.416
Teacher spread0.189 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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