Humor styles and the ten personality dimensions from the Supernumerary Personality Inventory
Bibliographic record
Abstract
Background The present study examines the relationship between humor styles and the 10 Supernumerary Personality Invento-ry (SPI) traits to understand how humor styles correlate with personality dimensions “beyond the Big Five” model. Humor styles and the personality dimensions of the SPI have yet to be explored. Therefore, the aim of this study is to explore how humor styles correlate with traits outside of conventional personality models, in order to better un-derstand humor expression related to personality traits. Participants and procedure The data were from 693 adult participants (135 men and 560 women) from North America. Results All four humor styles positively correlated with the SPI humorousness scale. The two positive humor styles, affiliative and self-enhancing, had significant positive correlations with the egotism SPI scale. The two negative humor styles, aggressive and self-defeating, had significant positive correlations with the SPI scales of seductiveness and manipu-lativeness and significant negative correlations with the integrity scale from the SPI. A sub-group of the sample (n = 471) also completed a Big Five personality measure. For this sample, the variance due to the Big Five was re-gressed out of the SPI scales. Conclusions The correlations between the SPI residuals and the humor style scores decreased from the unaltered SPI scale scores except for the aggressive humor style correlations, which were less affected, suggesting that this dimension of humor may have some variance “beyond” the Big Five.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".