What is the temperamental basis of humour like in China? A cross‐national examination and validation of the standard version of the state–trait cheerfulness inventory
Bibliographic record
Abstract
The State-Trait Cheerfulness Inventory-trait version (STCI-T60) consists of three dimensions of cheerfulness, seriousness, and bad mood integrated to measure the temperamental basis of the sense of humour. The present study replicated the three-dimensional factor structure of the STCI in China using 60 items consistent with other standard trait versions (e.g., English, Chilean-Spanish). Closer examination of associations between traits suggested bad mood showed curvilinear associations with both cheerfulness and seriousness, such that cheerfulness and bad mood were negatively associated for those low and average in trait bad mood but not for those with high trait bad mood. Seriousness was positively associated with bad mood at high levels of trait bad mood, but not at average or low levels of bad mood. Associations between the STCI traits and major personality dimensions, humour styles, and well-being were further examined. Cheerfulness and seriousness showed positive associations with satisfaction with life and emotional well-being (EWB) while bad mood showed a curvilinear association with EWB. Using multi-group confirmatory factor analyses, partial metric invariance was found between English and Chinese versions of the STCI-T60, but structural invariance was not observed. Implications based on the empirical literature in dialecticism and cross-cultural assessment were thoroughly discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".