LOWER LEVEL OF DRINK-DRIVING IN THE NETHERLANDS COINCIDES WITH INCREASED DRUG-DRIVING
Bibliographic record
Abstract
Drink-driving in The Netherlands has dropped significantly since the mid-eighties. In 1983, 12% of car drivers during weekend-nights were over the legal BAC-limit of 0.5 g/l. In the first half of the 1990s, this proportion had dropped to around 4%. In the second half of the 1990s, the proportion of illegal BACs increased slightly, stabilising around 4.5%. But, while drink-driving decreased substantially, the problem of drug-driving seemed to be growing, especially among young males. In the 1997/1998 sample, 6.4% of all urine tests turned out to be positive for one or more impairing drugs; 1% for medicaments like codeine and benzodiazepines, and 5.4% for illegal drugs. Of the illegal drugs, three quarters consisted of cannabis. The remaining quarter consisted of hard drugs, mostly cocaine in combination with cannabis. Among the drivers who tested positive for drugs, 12% had an illegal BAC. So, drug-driving correlates positively with drink-driving.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".