Measuring child personality when child personality was not measured: Application of a thin‐slice approach
Bibliographic record
Abstract
Recent efforts have demonstrated that thin-slice (TS) assessment-or assessment of individual characteristics after only brief exposure to that individual's behaviour-can produce reliable and valid measurements of child personality traits. The extent to which this approach can be generalized to archival data not designed to measure personality, and whether it can be used to measure personality pathology traits in youth, is not yet known. Archival video data of a parent-child interaction task was collected as part of a clinical intervention trial for aggressive children (N = 177). Unacquainted observers independently watched the clips and rated children on normal-range (neuroticism, extraversion, agreeableness, conscientiousness and openness to experience) and pathological (callous-unemotional) personality traits. TS ratings of child personality showed strong internal consistency, valid associations with measures of externalizing problems and temperament, and revealed differentiated subgroups of children based on severity. As such, these findings demonstrate an ecologically valid application of TS methodology and illustrate how researchers and clinicians can extend their existing data by measuring child personality using TS methodology, even in cases where child personality was not originally measured. Copyright © 2016 John Wiley & Sons, Ltd.
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 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".