Preventing Aggressive and Violent Behavior: Using Prevention Programs to Study the Role of Peer Dynamics in Maladjustment Problems
Bibliographic record
Abstract
Two processes in the relationships between children have been associated with adverse developmental outcomes in children, namely, peer rejection and affiliation with deviant peers. In numerous studies, both of these processes have been linked not only to negative outcomes, including aggression, delinquency, violence, but also to school failure or internalizing problems. However, we yet have to understand the exact role of peer dynamics in the development towards maladjustment. Many prevention programs aim at manipulating peer dynamics in their effort to prevent aggression and violence. Apart from studying the effectiveness of such programs, these programs can provide valuable insight through which peer dynamics are linked with aggression and violence. In this study, we review prevention programs that studied the role of peer dynamics in the development to aggression and violent behaviors. First, we shortly describe the processes of peer rejection and deviant friends’ affiliation. We then review three types of intervention programs: programs in unstructured settings, universal classroom-based programs, and targeted intervention aimed at the direct manipulation of peer processes. From these studies, we demonstrate that intervention success (and also intervention failure) is, to an extent, accounted for by peer dynamics. Implications for further research are discussed. We conclude that although preventive interventions that aim at manipulating the social context of children are promising, we should be cautious with a large-scale implementation of such programs as our knowledge of peer dynamics is yet too limited to ascertain the absence of unexpected negative effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".