Bullying perpetration and HEXACO personality: Examining between- and within-person longitudinal associations
Bibliographic record
Abstract
Previous findings indicate the utility of the HEXACO model in understanding the personality traits associated with bullying perpetration. However, these studies are all cross-sectional, limiting the understanding of how bullying and HEXACO personality develop alongside one another across adolescence. Therefore, we examined between- and within-person longitudinal associations between bullying and the HEXACO traits in a sample of 392 youth ( M age = 11.88 at Wave 1; SD = 1.42; 49.5% boys) across 3 years of development using random intercept cross-lagged panel models. As expected, we found a between-person association between higher bullying and lower Honesty-Humility, as well as within-person, within-time associations between bullying and lower Honesty-Humility. Unexpectedly, we did not find within-person cross-lagged paths. Our findings suggest that individuals higher on exploitative traits (i.e., lower Honesty-Humility) are more likely to be higher on bullying perpetration. In addition, individuals whose exploitative traits increase are likely to concurrently increase on bullying perpetration. Our findings highlight the importance of understanding developmental processes at the between- and within-person levels. Our results suggest that anti-bullying interventions might benefit from taking note of individuals who possess exploitative personality traits.
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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 | 0.005 |
| 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) | 0.002 | 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".