Are more humorous children more intelligent? A case from Turkish culture
Bibliographic record
Abstract
Abstract This study aimed to investigate the relationship between intelligence and humor ability in a Turkish sample. The sample included 217 middle-school students with a wide range of intelligence measured by a Turkish intelligence test (ASIS). Humor ability was measured using the Humor Ability Assessment Form. Students were instructed to write captions for 10 cartoons that were as funny and relevant as possible. Seven experts rated the funniness of the captions and their relevance to the cartoons, yielding a total of 30,380 ratings (217 students × 10 cartoons × two criteria × seven experts). The findings showed that both general intelligence and the second-level components (verbal ability, visual-spatial ability, and memory) had high correlations with humor ability. Intelligence explained 68% of the variance in humor ability. Among the third-level factors, verbal analogical reasoning was the primary predictor of humor ability (β = 0.325, p < 0.001). Humor ability scores significantly differed across intelligence clusters, implying that highly humorous children may be highly intelligent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".