Experimental effects of injunctive norms on simulated risky driving among teenage males.
Bibliographic record
Abstract
OBJECTIVE: Teenage passengers affect teenage driving performance, possibly by social influence. To examine the effect of social norms on driving behavior, male teenagers were randomly assigned to drive in a simulator with a peer-aged confederate to whom participants were primed to attribute either risk-accepting or risk-averse social norms. It was hypothesized that teenage drivers would engage in more risky driving behavior in the presence of peer passengers than no passengers, and with a risk-accepting compared with a risk-averse passenger. METHOD: 66 male participants aged 16 to 18 years holding a provisional driver license were randomized to drive with a risk-accepting or risk-averse passenger in a simulator. Failure to Stop at a red light and percent Time in Red (light) were measured as primary risk-relevant outcomes of interest at 18 intersections, while driving once alone and once with their assigned passenger. RESULTS: The effect of passenger presence on risky driving was moderated by passenger type for Failed to Stop in a generalized linear mixed model (OR = 1.84, 95% CI [1.19, 2.86], p < .001), and percent Time in Red in a mixed model (B = 7.71, 95% CI [1.54, 13.87], p < .05). CONCLUSIONS: Exposure of teenage males to a risk-accepting confederate peer increased teenage males' risky simulated driving behavior compared with exposure to a risk-averse confederate peer. These results indicate that variability in teenage risky driving could be partially explained by social norms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".