Breaking ground in cross-cultural research on the fear of being laughed at (gelotophobia): A multi-national study involving 73 countries
Bibliographic record
Abstract
Abstract The current study examines whether the fear of being laughed at (gelotophobia) can be assessed reliably and validly by means of a self-report instrument in different countries of the world. All items of the GELOPH (Ruch and Titze, GELOPH〈46〉, University of Düsseldorf, 1998; Ruch and Proyer, Swiss Journal of Psychology 67:19–27, 2008b) were translated to the local language of the collaborator (42 languages in total). In total, 22,610 participants in 93 samples from 73 countries completed the GELOPH. Across all samples the reliability of the 15-item questionnaire was high (mean alpha of .85) and in all samples the scales appeared to be unidimensional. The endorsement rates for the items ranged from 1.31% through 80.00% to a single item. Variations in the mean scores of the items were more strongly related to the culture in a country and not to the language in which the data were collected. This was also supported by a multidimensional scaling analysis with standardized mean scores of the items from the GELOPH〈15〉. This analysis identified two dimensions that further helped explaining the data (i.e., insecure vs. intense avoidant-restrictive and low vs. high suspicious tendencies towards the laughter of others). Furthermore, multiple samples derived from one country tended to be (with a few exceptions) highly similar. The study shows that gelotophobia can be assessed reliably by means of a self-report instrument in cross-cultural research. This study enables further studies of the fear of being laughed at with regard to differences in the prevalence and putative causes of gelotophobia in comparisons to different cultures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".