An In-depth Examination of Personality and Aggression Across Different Contexts
Bibliographic record
Abstract
Acts of aggression are associated with a variety of negative outcomes. Accordingly, research has aimed to identify the personality traits that give rise to different forms of aggressive behaviour. Recent work has indicated that the factor of Honesty-Humility is associated with a variety of deviant behaviours, including aggression towards others; however, the nuances of these relationships require further investigation. This dissertation aimed to address several gaps in this literature through three main studies. In Study 1, we extended previous findings to younger populations, examining the associations between Honesty-Humility and aggression longitudinally in a large sample of children and youth. These findings demonstrated a bidirectional relationship between Honesty-Humility and aggression over time, such that low levels of Honesty-Humility resulted in higher levels of aggression and vice versa. In Study 2, we explored the specific facets of Honesty-Humility to determine if they differentially predict proactive and reactive aggression. Despite the theoretical link between Modesty and reactive aggression, we found limited support for this association, especially when controlling for proactive aggression. Overall, the Sincerity and Fairness facets were found to strongly predict both forms of aggression. Lastly, Study 3 explored the associations between Honesty-Humility and deviance, aggression, exploitation, and victimization in a workplace context. Robust relationships were found between Honesty-Humility and several deviant behaviours, further emphasizing the importance of this trait. In particular, when provided with the opportunity to aggress, individuals low in Honesty-Humility were more likely to do so, regardless of their level of power in the situation. Collectively, these findings indicate that Honesty-Humility is the strongest predictor of aggressive and deviant behaviour among the broad factors of personality. However, this dissertation extends previous findings by demonstrating the applicability of Honesty-Humility across different contexts and by providing a nuanced understanding of the components responsible for this relationship.
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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 | 0.001 |
| 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".