Child and context characteristics in trajectories of physical and relational victimization among early elementary school children
Bibliographic record
Abstract
Transactional models suggest that peer victimization results from both individual and context differences, and understanding these differences may point to important targets for prevention and interventions that reduce victimization. Multilevel modeling was used to examine within-person (aggression and emotional dysregulation), between-person (sex and age), and between-school (participation in a victimization prevention program) factors that influence changes in physical and relational victimization over the first three years of elementary school. Children (n = 423) reported their experiences of peer victimization at entry into Grade 1 and at the end of Grade 1, Grade 2, and Grade 3. On average, trajectories of both physical and relational victimization declined. However, for individual children, teacher-rated aggression was associated with increases in physical and relational victimization, while emotional dysregulation was associated with attenuation of longitudinal declines in physical victimization and increases in relational victimization. Individual differences in sex and age at entry into Grade 1 did not significantly influence victimization trajectories over Grades 1 to 3. Children who participated in the WITS® victimization prevention program showed significant declines in physical and relational victimization. Levels of victimization among nonparticipants remained stable. Implications of child and context characteristics for preventing peer victimization in elementary school 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".