Testing the Developmental Distinctiveness of Male Proactive and Reactive Aggression With a Nested Longitudinal Experimental Intervention
Bibliographic record
Abstract
An experimental preventive intervention nested into a longitudinal study was used to test the developmental distinctiveness of proactive and reactive aggression. The randomized multimodal preventive intervention targeted a subsample of boys rated disruptive by their teachers. These boys were initially part of a sample of 895 boys, followed from kindergarten to 17 years of age. Semiparametric analyses of developmental trajectories for self-reported proactive and reactive aggression (between 13 and 17 years of age) indicated three trajectories for each type of aggression that varied in size and shape (Low, Moderate, and High Peaking). Intent-to-treat comparisons between the boys in the prevention group and the control group confirmed that the preventive intervention between 7 and 9 years of age, which included parenting skills and social skills training, could impact the development of reactive more than proactive aggression. The intervention effect identified in reactive aggression was related to a reduction in self-reported coercive parenting. The importance of these results for the distinction between subtypes of aggressive behaviors and the value of longitudinal-experimental studies from early childhood onward is 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".