Understanding driver anger and aggression: Attributional theory in the driving environment.
Bibliographic record
Abstract
Two studies tested the applicability of Weiner's (1995, 1996, 2001, 2006) attributional model of social conduct to roadway environments. This model highlights the role of inferences of responsibility after making causal judgments for social transgressions. Study 1 employed written scenarios where participants were asked to imagine themselves driving on a major highway. The degree of controllability and intentionality of the driving act was manipulated experimentally by altering the specific event-related details provided to the participants. Study 2 extended this research to life events by having participants complete online driving diaries every 2 days, identifying their most negative/upsetting encounter with another motorist. The most anger-provoking event was selected from among 4 diary entries and participants were asked to respond to a questionnaire similar to that used in Study 1. Path analyses in both studies generally supported predictions derived from Weiner's model; the association between perceived controllability, intentionality, and dispositional locus of causality of the negative driving event and subsequent anger was mediated by perceptions of responsibility. Additional results in Study 2 suggested that low perceived controllability, intentionality, and dispositional locus of causality were associated with reduced perceived responsibility, which, in turn, facilitated feelings of sympathy. Anger was associated with aggressive responses to the offending driver, whereas sympathy was associated with prosocial responses. Recommendations were offered for improved driver safety, including the development of attributional retraining programs to combat self-serving attributional biases, teaching novice drivers about both formal and informal roadway communication, and the promotion of forgiveness among drivers
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".