A longitudinal twin study of callous-unemotional traits during childhood.
Bibliographic record
Abstract
Previous research indicates that genetic factors largely account for the stability of callous-unemotional (CU) traits in adolescence. However, the genetic-environmental etiology of the development of CU traits has not been extensively investigated in childhood, despite work showing the reliable measurement and stability of CU traits from a young age. The aim of this study was to investigate the temporal pattern of genetic and environmental etiology of CU traits across primary school, from school entry (7 years) to middle (9 and 10 years) and late childhood (12 years). Data were collected in a population sample of twins composed of 662 twin pairs (Quebec Newborn Twin Study). CU traits were reported by teachers and analyzed using a biometric latent growth curve model and a Cholesky decomposition model. Latent growth curve analyses revealed that genetic factors explain most of the variance in the intercept of CU traits. Individual differences in change over time were not significant. The Cholesky model revealed that genetic factors at 7 years had enduring contributions to CU traits at 9, 10, and 12 years. New, modest genetic contributions appeared at 9 and 10 years. Nonshared environmental contributions were generally age-specific. No shared environmental contributions were detected. In sum, both modeling approaches showed that genetic factors underlie CU traits during childhood. Initial and new genetic contributions arise during this period. Environments have substantial contributions, over and above genetic factors. Future research should investigate the source of genetic risk associated with CU traits. (PsycINFO Database Record
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".