Strong genetic effects on cross‐situational antisocial behaviour among 5‐year‐old children according to mothers, teachers, examiner‐observers, and twins’ self‐reports
Bibliographic record
Abstract
BACKGROUND: Early childhood antisocial behaviour is a strong prognostic indicator for poor adult mental health. Thus, information about its etiology is needed. Genetic etiology is unknown because most research with young children focuses on environmental risk factors, and the few existing studies of young twins used only mothers' reports of behaviour, which may be biased. METHOD: We investigated genetic influences on antisocial behaviour in a representative-plus-high-risk sample of 1116 pairs of 5-year-old twins using data from four independent sources: mothers, teachers, examiner-observers previously unacquainted with the children, and the children themselves. RESULTS: Children's antisocial behaviour was reliably measured by all four informants; no bias was detected in mothers', teachers', examiners', or children's reports. Variation in antisocial behaviour that was agreed upon by all informants, and thus was pervasive across settings, was influenced by genetic factors (82%) and experiences specific to each child (18%). Variation in antisocial behaviour that was specific to each informant was meaningful variation, as it was also influenced by genetic factors (from 33% for the children's report to 71% for the teachers' report). CONCLUSIONS: This study and four others of very young twins show that genetic risks contribute strongly to population variation in antisocial behaviour that emerges in early childhood. In contrast, genetic risk is known to be relatively modest for adolescent antisocial behaviour, suggesting that the early-childhood form has a distinct etiology, particularly if it is pervasive across situations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".