The subjective assessment of the fear of being laughed at (gelotophobia): French adaptation of the GELOPH<15> questionnaire
Bibliographic record
Abstract
This paper describes the adaptation of the French version of the GELOPH<15>, a short questionnaire (15 items) that measures the fear of being laughed at (gelotophobia). The first empirical studies revealed that gelotophobia is a valid and useful new concept that should be interpreted as an individual differences phenomenon within the range of normality. Prior studies suggested that the GELOPH<15> is reliable, and showed a one-dimensional factor solution. In order to test psychometric properties of the French version, N = 218 participants from the French part of Switzerland and N = 245 participants from Quebec (French Canada) took the test. One item that yielded a particularly high endorsement pertained to the interpretation of others' laughter as being laughter at oneself (Switzerland); another item with the same result reflected how unease experienced while dancing is due to the conviction that one is being assessed negatively by others (Quebec). The fear of being laughed at was independent of the participants' age, sex or marital status. Additionally, the mean gelotophobia scores in the French Canadian and Swiss samples did not differ from each other. The French version of the GELOPH<15> provides a useful and reliable instrument for the subjective assessment of gelotophobia in French-speaking countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".