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Record W3037241699 · doi:10.1515/humor-2019-0027

Humor style differences across four English-speaking countries

2020· article· en· W3037241699 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHumor - International Journal of Humor Research · 2020
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyStyle (visual arts)Developmental psychologyScale (ratio)Social psychologyClinical psychologyGeography

Abstract

fetched live from OpenAlex

Abstract Using three archival data sets, mean differences in the four humor styles of affiliative, self-enhancing, aggressive, and self-defeating were assessed for adults ( n = 6404) across four English-speaking countries: Canada ( n = 339), the USA ( n = 165), the United Kingdom ( n = 4012), and Australia ( n = 1888). As age and sex varied greatly across the samples and had significant relationships with the humor styles (men scored higher on each scale, younger people scored higher on affiliative, aggressive, and self-defeating humor, and older people scored higher on self-enhancing humor), age and sex were regressed out of the humor style scores and the standardized residuals were examined. Significant differences were found for the four humor styles. Specifically, the Americans were the highest in affiliative and self-enhancing humor, and the British were the highest in both aggressive and self-defeating humor. As humor styles are an insight into human social interactions, the results provide a glimpse into the differences found between these countries.

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.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.261
GPT teacher head0.497
Teacher spread0.237 · 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