Graduated driver licensing. [previously called: The graduated driving licence.]
Bibliographic record
Abstract
Young novice drivers have a very high risk of being involved in a road crash. In the United States (US), Canada, Australia and New Zealand this problem has been tackled by first letting learner drivers gain driving experience under safe conditions before allowing them to take the driving test. The more driving experience learner drivers have gained, the more they are allowed to drive under less safe conditions. In these countries this so-called ‘graduated driver licensing’ system has resulted in a considerable decline in the number of crashes involving young novice drivers. However, the decline cannot so much be attributed to having gained experience under protective conditions which makes them a better driver, but rather to the fact that it takes longer before they, as young novice drivers, are exposed to hazardous traffic conditions. In the Netherlands, the first steps towards a graduated driving licence have been taken with the introduction of accompanied driving (2toDrive) and the beginner’s licence (a lower alcohol limit and a demerit points system). Between the phases of accompanied driving and the beginner’s licence, the graduated licensing system has an intermediate phase in which solo driving is allowed with restrictions: no driving when it is dark and no driving with peers as passengers. Such an intermediate phase does not exist in the Netherlands or in most other European countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.063 |
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".