Measuring Gelotophobia, Gelotophilia, and Katagelasticism in Italy and Canada Using PhoPhiKat-30
Bibliographic record
Abstract
Abstract: The PhoPhiKat-30 is a self-report instrument for describing personality related to laughter and ridicule including gelotophobia, gelotophilia, and katagelasticism. The present study assessed the measurement properties of the newly translated Italian PhoPhiKat-30 across participants in Italy and Canada using multidimensional item response theory. Italian ( N = 326) and Canadian ( N = 1,467) participants completed the Italian and English PhoPhiKat-30, respectively. The parallel analysis supported the three-factor model in Italy. Conditional reliability estimates showed strong precision (> 0.80) of gelotophobia and gelotophilia along the latent continuum (−1.15 < θ < 3.08 and −1.69 < θ < 3.09, respectively). Katagelasticism showed a limited range (0.98 < θ < 2.85) for the latent attribute precisely measured, suggesting that new items that address the low to moderate difficulty of katagelasticism should be added in future studies. Item discrimination parameters varied across Reckase’s multidimensional normal-ogive model (MDISC mean = 0.79). Five items had uniform differential item functioning (DIF; McFadden’s pseudo R2 > .035 or β > .10) when comparing the Italian and English PhoPhiKat-30, with English items showing more agreement at the same level of the latent trait. The Italian PhoPhiKat-30 has good item discrimination across the latent continuum and showed cross-cultural equivalence for most items.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".