Implementing graduated driving license in Europe: literature review on practices and effects, and recommendation of an ideal model
Bibliographic record
Abstract
Graduated Driving License (GDL) systems are since many decades applied in the USA, Canada, Australia and New‐Zealand. GDL‐systems traditionally include three phases. During the first “supervised learning” phase the learner driver can only drive a vehicle when accompanied by an experienced driver. This allows experiencing different traffic situations while being supervised. In the second “autonomous practicing” phase the learner driver can drive alone on the road but under strict restrictions, like no driving at night or with passengers of the same age. This allows automating some driving abilities while avoiding specific situations with increased risk. In the third phase one has a full driving license without restrictions, although sometimes more severe sanctions are foreseen. The idea behind GDL is allowing learner drivers to gain driving experience gradually and with less exposure to risky traffic situations. The main idea behind is “learning through experience”. Although European systems traditionally focus on “learning through (professional) instruction”, an increased attention for the GDL‐approach is seen the last decennia. This is related to the increased opinion that learning to drive safely in traffic does require a long learning time and much practice, and this also goes along with insights on higher order driver educational goals (Goals for Driver Education matrix–GDE). This article synthesizes recent literature on effectiveness of GDL‐systems as well as on current tendencies in Europe that approach the GDL‐structure. Recommendations are formulated and an ideal structure for category B
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".