Evidence of gene–environment correlation for peer difficulties: Disruptive behaviors predict early peer relation difficulties in school through genetic effects
Bibliographic record
Abstract
Early disruptive behaviors, such as aggressive and hyperactive behaviors, known to be influenced by genetic factors, have been found to predict early school peer relation difficulties, such as peer rejection and victimization. However, there is no consensus regarding the developmental processes underlying this predictive association. Genetically informative designs, such as twin studies, are well suited for investigating the underlying genetic and environmental etiology of this association. The main goal of the present study was to examine the possible establishment of an emerging gene-environment correlation linking disruptive behaviors to peer relationship difficulties during the first years of school. Participants were drawn from an ongoing longitudinal study of twins who were assessed with respect to their social behaviors and their peer relation difficulties in kindergarten and in Grade 1 through peer nominations measures and teacher ratings. As predicted, disruptive behaviors were concurrently and predictively associated with peer relation difficulties. Multivariate analyses of these associations indicate that they were mainly accounted for by genetic factors. These results emphasize the need to adopt an early and persistent prevention framework targeting both the child and the peer context to alleviate the establishment of a negative coercive process and its consequences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".