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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".