Longitudinal patterns of anger reactivity and risk‐taking: The role of peer‐context
Bibliographic record
Abstract
The current study examined the interplay between children's dispositional anger and susceptibility to peers' influence in increasing children's risk-taking behaviors. Participants in the current study were children from a larger study of temperament and social-emotional development who were followed across 9, 24, 36, 48, and 60 months. Dispositional anger was measured using mothers' reports across 9 and 48 months. At 60 months, children played a risk-taking computer game in presence of an unfamiliar peer who watched the child play. The child's risk-taking was assessed during the game as the unfamiliar peers' reactions were coded based on comments that were peer directed, reflective of praising the target child's performance, or object directed, indicative of excitement toward the game. A latent profile analysis revealed three longitudinal anger profiles across infancy to early childhood: high stable, average stable, and low stable anger. Results suggested that as peers' object-directed comments predicted risk-taking independent of children's anger, the association between peer-directed comments and risk-taking was dependent on children's dispositional anger. Specifically, when peers praised the target child's performance, children in the high stable anger profile showed increased risk-taking propensity. Findings are discussed based on the importance of considering both temperamental characteristics and aspects of the peer context in relation to children's risk-taking.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".