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Record W2406902470 · doi:10.1017/thg.2012.23

A Behavioral Genetic Study of Humor Styles in an Australian Sample

2012· article· en· W2406902470 on OpenAlexaff
Holly M. Baughman, Erica A. Giammarco, Livia Veselka, Julie Aitken Schermer, Nicholas G. Martin, Michael T. Lynskey, Phillip A. Vernon

Bibliographic record

VenueTwin Research and Human Genetics · 2012
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsWestern University
FundersNational Institute on Drug AbuseNational Institutes of Health
KeywordsPsychologyDizygotic twinsSample (material)Twin studyDevelopmental psychologyClinical psychologyDemographyHeritabilityMedicineGeneticsBiology

Abstract

fetched live from OpenAlex

The present study investigated the extent to which individual differences in humor styles are attributable to genetic and/or environmental factors in an Australian sample. Participants were 934 same-sex pairs of adult twins from the Australian Twin Registry (546 monozygotic pairs, 388 dizygotic pairs) who completed the Humor Styles Questionnaire (HSQ). The HSQ measures four distinct styles of humor - affiliative, self-enhancing, aggressive, and self-defeating. Results revealed that additive genetic and non-shared environmental factors accounted for the variance in all four humor styles, thus replicating results previously obtained in a sample of twins from the United Kingdom. However, a study conducted with a U.S. sample produced different results and we interpret these findings in terms of cross-cultural differences in humor.

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)0.004
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.395
GPT teacher head0.554
Teacher spread0.159 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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