Aggression can be contagious: Longitudinal associations between proactive aggression and reactive aggression among young twins
Bibliographic record
Abstract
The present study examined sibling influence over reactive and proactive aggression in a sample of 452 same-sex twins (113 male dyads, 113 female dyads). Between and within siblings influence processes were examined as a function of relative levels of parental coercion and hostility to test the hypothesis that aggression contagion between twins occurs only among dyads who experience parental coerciveness. Teacher reports of reactive and proactive aggression were collected for each twin in kindergarten (M = 6.04 years; SD = 0.27) and in first grade (M = 7.08 years; SD = 0.27). Families were divided into relatively low, average, and relatively high parental coercion-hostility groups on the basis of maternal reports collected when the children were 5 years old. In families with relatively high levels of parental coercion-hostility, there was evidence of between-sibling influence, such that one twin's reactive aggression at age 6 predicted increases in the other twin's reactive aggression from ages 6 to 7, and one twin's proactive aggression at age 6 predicted increases in the other twin's proactive aggression from ages 6 to 7. There was also evidence of within-sibling influence such that a child's level of reactive aggression at age 6 predicted increases in the same child's proactive aggression at age 7, regardless of parental coercion-hostility. The findings provide new information about the etiology of reactive and proactive aggression and individual differences in their developmental interplay.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".