Vulnerable and rebellious: The instability of aggressive drivers
Bibliographic record
Abstract
Aggressive driving contributes to a significant number of vehicle crashes every year and remains a prevalent behavior despite traffic enforcement.Our study investigates the constituents of three known predictors of aggressive driving: self-esteem, narcissism, and rebelliousness.We administered an online survey to 194 undergraduates who were recruited through a psychology participant pool from a mid-sized Canadian university.Correlational and regression analyses revealed that aggressive driving behaviors were predicted by vulnerable narcissism, proactive rebelliousness, and reactive rebelliousness.Additionally, both grandiose and vulnerable narcissism were significantly correlated with both proactive and reactive rebelliousness.Hierarchical regression analyses showed that the two types of rebelliousness contributed to 19% of the variance in aggressive driving when controlling for vulnerable narcissism.Lastly, mediation analyses revealed that both proactive and reactive rebelliousness partially mediated the relationship between vulnerable narcissism and aggressive driving behaviors.These results suggest that both types of rebelliousness play a significant role in aggressive driving behaviors.We also encourage future research that examines negativistic dominance and self-esteem instability as predictors of aggressive behavior.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".