“Do You Trust Him?” Children’s Trust Beliefs and Developmental Trajectories of Aggressive Behavior in an Ethnically Diverse Sample
Bibliographic record
Abstract
This study investigated the role of trust beliefs (i.e., trustworthiness, trustfulness) on aggression trajectories in a four-wave longitudinal study using an ethnically diverse sample of 8- to 11-year-old children (N = 1,028), as well as the risk profiles of low trust beliefs and low socioeconomic status on aggression trajectories. At Time 1 to Time 4, teachers provided ratings of overt aggressive behavior. At Time 1, children's trust beliefs were assessed by a sociometric peer nomination instrument and derived using social relations analysis. Latent growth curve analysis revealed five trajectories of aggressive behavior: high-stable, medium-stable, low-stable, increasing, and decreasing. As hypothesized, children in the high-stable trajectory were perceived as less trustworthy than children in the low-stable, medium-stable, and increasing trajectories. Children in the high-stable trajectory were less trustful than children in the low-stable trajectory and had a significantly higher risk profile (i.e., low trust beliefs and low SES) compared to children in the low-stable trajectory. Our findings indicate that the developmental course of aggression during middle childhood is predicted by children's trustworthiness and trustfulness. A risk profile of low trust and low socioeconomic status contributes to high-stable aggression trajectories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".