Investigating honesty‐humility and impulsivity as predictors of aggression in children and youth
Bibliographic record
Abstract
Among adult and adolescent populations, the personality trait of honesty-humility (HH) has been linked to aggression. For example, adults low in HH have been found to exhibit higher levels of workplace delinquency and revenge motivation, and adolescent low in HH are more likely to bully others. However, there is a paucity of research examining this relationship in children and youth, and how these relationships develop over time. The current study addressed these gaps in the literature by assessing whether HH and impulsivity are independently associated with aggression in children Grades 3 through 8 (N = 1201). Using data from the two waves of a longitudinal project, autoregressive crossed-lagged path analysis was used to examine the bidirectional relationships between HH, impulsivity, and aggression over a 1-year period. Results revealed significant bidirectional relationships between HH and aggression, such that lower scores of HH at Time 1 were associated with higher scores of aggression at Time 2 and vice versa. Similarly, higher scores of impulsivity at Time 1 were associated with higher scores of aggression at Time 2 and vice versa. In addition, these relationships were strongest in boys and at higher ages. Consistent with research in other populations, these results indicate that low HH and high impulsivity are linked to aggression in children and youth. Further, our results demonstrate that HH and impulsivity bidirectionally impact aggression as one age, suggesting a need for early intervention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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".