Licensing Age Issues: Deliberations from a Workshop Devoted to this Topic
Bibliographic record
Abstract
OBJECTIVE: To highlight the issues and discuss the research evidence regarding safety, mobility, and other consequences of different licensing ages. METHODS: Information included is based on presentations and discussions at a 1-day workshop on licensing age issues and a review and synthesis of the international literature. RESULTS: The literature indicates that higher licensing ages are associated with safety benefits. There is an associated mobility loss, more likely to be an issue in rural states. Legislative attempts to raise the minimum age for independent driving in the United States--for example, from 16 to 17--have been resisted, although in some states the age has been raised indirectly through graduated driver licensing (GDL) policies. CONCLUSIONS: Jurisdictions can achieve reductions in teenage crashes by raising the licensing age. This can be done directly or indirectly by strengthening GDL systems, in particular extending the minimum length of the learner period. Supplementary materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention for the following supplemental resource: List of workshop participants.
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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 | 0.044 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.032 | 0.039 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".