Humorous peer play and social understanding in childhood
Bibliographic record
Abstract
Humour plays a crucial role in children's early interactions, likely promoting the development of social understanding and fostering positive social relationships. To date, the connection between humour production in peer play and the development of social understanding skills in middle childhood has received limited attention. In a community sample of 130 children residing in the UK (M = 6.16 years old, range 5-7; 67 [51.5%] girls, 62 [47.7%] boys, and 1 [0.8%] non-binary child; 95 [73.1%] mothers and 85 [65.4%] fathers identified as Welsh, English, Scottish, or Irish), we tested our prediction that children's use of humour in play with peers would be positively associated with children's ability to understand the minds of others. We conducted detailed observational coding of children's humour production during peer play and examined associations with children's performance on a battery of social understanding assessments. Multilevel models showed that 42.8% of the variance in children's humour production was explained by play partner effects. When controlling for the effect of play partner and other individual child characteristics (age, gender, receptive vocabulary) children's spontaneous attributions of mental states were associated with humour production. Results are discussed considering how these playful exchanges reflect and influence the development of children's socio-cognitive competencies.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".