Do Responses Differ between Novice and Experienced Drivers when a Late Yellow Light Is Encountered?
Bibliographic record
Abstract
To understand why novice teenage drivers are overrepresented in fatal and injury crashes, a longitudinal simulator study examined the development of driver competence and hazard perception during the first six months post graduated licensure. The performance of 16 novice teenage drivers ( M = 16.2 years of age) was compared to a group of 15 experienced adult drivers ( M = 32.9) when a variety of maneuvers and hazards were encountered. Participants attended monthly simulator sessions over a period of six months. The results of the late yellow light event are presented. Drivers were either followed or not followed by another vehicle as they traveled through a number of intersections during the course of two experimental drives per session. The percentage of novice and experienced drivers who ran the late yellow light did not differ nor did approach speeds to the intersection, although several trends across sessions were consistent with increased novice driver risk taking or over-confidence. The presence of a following vehicle did not affect the decision to run the yellow light, but fewer novice drivers actively sampled the rearview mirror. Interestingly, novice drivers were significantly more likely to be caught in the intersection once the light turned red.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".