Unlicensed Driving Worldwide – The Scope of the Problem and Countermeasures
Bibliographic record
Abstract
The problem of unlicensed driving is a major road safety problem in countries around the world. A survey in the U.S. showed that 20 percent- one in every five- of all fatal crashes in the U.S. involve drivers who should never have been on the road in the first place due to the fact they were unlicensed. A study in Queensland, Australia showed that unlicensed drivers represented 7.9 % of drivers involved in fatal crashes. The study results suggest that unlicensed drivers are more likely to engage in higher risk behaviors than licensed drivers. A 2006 study, conducted for MADD Canada, found that suspended drivers in Saskatchewan continued to drive with no license and that these drivers were at higher risk to be in an automobile crash. In Ontario, one in fourteen fatal crashes involved an unlicensed driver. European countries have also found that unlicensed drivers pose a higher risk. For example, in the U.K., breath test results showed that 17 % of disqualified drivers who were breath tested after an accident were positive compared with the national average for all post-crash tests of 3%. A 2004 U.K. Department for Transport report found that there are around a million unlicensed drivers on U.K. roads. The report notes that while unlicensed drivers account for less than one percent of total hours driven, unlicensed drivers are up to nine times more likely to have an accident than licensed drivers. Data also indicates that the problem is increasing in France and Belgium. This paper will discuss the worldwide scope of the problem and potential solutions, including the impoundment of the vehicles of those who are driving illegally.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".