Psychological Predictors of Environmentally Unsustainable Driving Behaviors: Schadenfreude and Preference for Loud Car Modifications
Bibliographic record
Abstract
Noise pollution from modified vehicles represents a growing environmental and social sustainability concern in urban areas. Understanding the psychological factors underlying such behaviors is essential for promoting sustainable mobility and public well-being. The present study investigates whether schadenfreude (i.e., the enjoyment of others’ misfortune) predicts attitudes toward loud car modifications, an environmentally unsustainable behavior linked to social disturbance. University undergraduate students (N = 606; 61% men) completed an online self-report survey assessing sex, age, schadenfreude across three scenarios, and attitudes toward loud cars. Multiple regression analyses revealed that men with lower scores on two schadenfreude scenarios and higher scores on the third were more likely to report favorable attitudes toward loud car modifications. These findings extend previous research on personality and antisocial driving tendencies, highlighting the relevance of emotional traits in understanding behaviors that undermine sustainable urban environments. Future interventions addressing social and psychological drivers of noise pollution may contribute to environmental and social sustainability efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".