Personality and Metamotivational Predictors of Aggressive Driving
Bibliographic record
Abstract
Aggressive driving is an all-too common occurrence and a major cause of serious motor vehicle accidents.This investigation examined relations among individuals' personality and metamotivational tendencies and their propensity for aggressive driving.In Phase 1 of this research, 270 participants recruited from the psychology participant pool of a medium-sized Canadian university completed an online survey that examined propensity for aggressive driving in relation to driving behaviors and measures of personality and motivation, including the Motivational Style Profile (Apter et al., 1998).Phase 2 was a pilot study of 12 participants who took part in a driving simulator experimental session in which participants' reactions to driving scenarios that are intended to elicit aggressive driving were observed and recorded, and pre-and posttest measures of physiological indicators and mood states were administered.Findings from Phase 1 indicated that individuals who were higher in narcissism, impulsivity, negativism, and paratelic dominance showed greater propensity for aggressive driving.In addition, individuals who were high in negativism reported less consistent use of seatbelts, and more negative driving incidents such as previous accidents and incidents of inattentive driving.Negativism was also found to be significantly associated with propensity for aggressive driving in the Phase 2 pilot study, and negativist dominant individuals were found to show decreases in negative mood state and physiological arousal after the simulated driving, relative to conformist dominant participants.Implications of considering metamotivational predictors of aggressive driving are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".