Evaluation of New Jersey's Graduated Driver Licensing Program
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate New Jersey's unique combination of a higher licensing age and a strong GDL system applicable to all novice drivers. METHODS: Population-based crash rates for drivers of ages potentially affected by GDL were compared, pre- and post-GDL implementation, with those of adults ages 25-59, using data on fatal crashes and on all police-reported crashes. RESULTS: After GDL implementation, there were statistically significant reductions in the crash rates of 17-year-olds, based on all reported crashes (16%), injury crashes (14%), and fatal crashes (25%), relative to those of drivers ages 25-59. The crash rates of 18-year-olds decreased significantly on the basis of all reported crashes (10%) and injury crashes (10%), relative to those of drivers ages 25-59. The fatal crash involvement rate of 18-year-olds decreased by 4 percent, which was not statistically significant. There was also a statistically significant reduction in fatal crashes of 16-year-old drivers; however, this is unlikely to have been attributable to GDL. Significant reductions in nighttime crashes (of all severity levels) of drivers ages 17 and 18 were observed, as were significant yet smaller reductions in their daytime crash rates. Reductions in fatal crashes of 17- and 18-year-olds carrying more than one passenger were sizable (23 and 24%, respectively) but were not statistically significant. CONCLUSIONS: New Jersey's licensing age of 17 eliminates most crashes at age 16. To the extent that the relative inexperience of 17-year-old drivers may negatively impact their crash rates, this effect appears to be largely blunted by New Jersey's strong GDL system. New Jersey's GDL system also reduces crashes at age 18, an age group untouched by other states' GDL systems. New Jersey's combination of licensing policies for young drivers is a model for the nation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".