Reckless driving promotion and prevention: priming effects
Bibliographic record
Abstract
Purpose This paper aims to examine how reckless driving scenes in action movies affect young male drivers’ perception of reckless drivers and proposes a targeted social marketing strategy to counteract this effect. Design/methodology/approach The hypotheses were tested through a 2 (reckless driving scenes vs control) × 2 (road safety advertising vs control) online experiment with 151 young male drivers. Findings Reckless driving scenes in action movies prime a positive image of reckless drivers which impacts young male drivers’ attitudes and reckless driving intention. However, a road safety message specifically addressing the positive image of reckless drivers efficiently counteracts this effect. Research limitations/implications A few studies have experimentally tested the impact of reckless driving promotion on young drivers’ attitudes and intention, but none have analysed this impact in terms of the development of a positive image of reckless drivers. In addition, this study emphasises that a targeted message based on social norms can cancel the effect of reckless driving promotion and have a beneficial impact on the most risk-prone drivers. Practical implications Social marketers working in the field of road safety can improve the efficacy of their social marketing programmes by taking into consideration the positive image of reckless drivers promoted by the media. Social implications Practitioners should develop interventions and targeted messages that help young drivers cultivate a less idealised and masculine social image of reckless drivers. Originality/value This paper enhances the awareness of the effect that the media’s positive depiction of reckless drivers can have on the youth and proposes a strategy to counteract this effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".