Thin slices of child personality: Perceptual, situational, and behavioral contributions.
Bibliographic record
Abstract
The present study examined whether thin-slice ratings of child personality serve as a resource-efficient and theoretically valid measurement of child personality traits. We extended theoretical work on the observability, perceptual accuracy, and situational consistency of childhood personality traits by examining intersource and interjudge agreement, cross-situational consistency, and convergent, divergent, and predictive validity of thin-slice ratings. Forty-five unacquainted independent coders rated 326 children's (ages 8-12) personality in 1 of 15 thin-slice behavioral scenarios (i.e., 3 raters per slice, for over 14,000 independent thin-slice ratings). Mothers, fathers, and children rated children's personality, psychopathology, and competence. We found robust evidence for correlations between thin-slice and mother/father ratings of child personality, within- and across-task consistency of thin-slice ratings, and convergent and divergent validity with psychopathology and competence. Surprisingly, thin-slice ratings were more consistent across situations in this child sample than previously found for adults. Taken together, these results suggest that thin slices are a valid and reliable measure to assess child personality, offering a useful method of measurement beyond questionnaires, helping to address novel questions of personality perception and consistency in childhood.
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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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".