The developmental relation between aggressive behaviour and prosocial behaviour: A 5-year longitudinal study
Bibliographic record
Abstract
BACKGROUND: Past research has shown links between both children's aggressive behaviour and a lack of prosocial behaviour to later maladaptation. Both types of behaviours have also been identified as crucial in children's social and emotional development and later (mal)adaptation. However, little is known about the way they predict each other over time. METHODS: We utilised a large, ethnically diverse, longitudinal population sample of girls and boys (N = 1,334) to examine the bidirectional cross-lagged links between aggressive and prosocial domains of behaviour from age seven to eleven. Teacher, parent and child self-reports were utilised to assess aggressive behaviour and prosocial behaviour. RESULTS: The results revealed that aggressive behaviour measured one year predicted decreases in prosocial behaviour in the following year. Conversely, prosocial behaviour did not predict changes in aggressive behaviour in the subsequent year. Furthermore, peer difficulties were examined and found to be an important mediator of the link between aggressive and prosocial behaviour. Specifically, peer difficulties mediated the links between aggressive behaviour and prosocial behaviour one year later, particularly during the first three years of school attendance. CONCLUSIONS: Implications of the findings for the design of intervention strategies to reduce children's aggressive behaviour are discussed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".