The Influence of Passengers on Driving in Young Drivers with Varying Levels of Experience
Bibliographic record
Abstract
Young drivers are at disproportionate risk of collision. It is unclear whether it is age or lack of driving experience that is the problem because age and experience are confounded in most studies (experienced drivers are typically much older). This study focused on drivers who were about the same age: all within the critical first years of skill development. We compared drivers just starting to drive (learner’s license) with those with a full license. Young drivers are especially at risk when driving with passengers. Consequently, we were interested in how the ability to drive with passengers changes in these first years. Driving performance was measured in a driving simulator when the passenger was absent (Absent condition), and when there was a passenger who was either asking the driver questions or was silent (Talking and Silent conditions). As predicted, the experienced young drivers had lower hazard response times and fewer collisions. Similarly, as predicted, performance was worse in the Talking condition, insofar as more drivers missed their turnoff in the way-finding task (where they were required to arrive at a certain destination using signs and landmarks). However, there were also interactive effects of experience and condition. In-vehicle conversation had an especially negative effect on the least experienced drivers, producing more collisions. Conversely, the more experienced young drivers sped up when they were driving with a passenger who talked with them. There was little difference between Silent and Absent conditions for all measures. This suggests in-vehicle conversation may be the critical factor.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".