Bibliographic record
Abstract
This chapter summarizes recent trends in a number of industrialized countries around the world and discusses the reasons for the changes that have occurred. It also reviews current programs designed to produce further reductions in impaired driving. In the decade of the 1980s, there were impressive declines in drinking and driving in much of the industrialized world. The declines included about 50 % in Great Britain, 28 % in Canada and The Netherlands, 32 % in Australia, 37 % in Germany and 26 % in the U.S. These declines did not continue in the early part of the 1990s. In some countries, there were actually increases. Toward the middle and latter part of the decade the increases stabilized and we again began to see some decreases. However, these decreases have been at a slower rate than the dramatic decreases in the 1980s. Toward the end of the 1990s and in the new century, the record has been mixed. Clear trends have emerged. Some countries (France and Germany) continued to reduce drinking and driving while in other countries (Australia, Canada, The Netherlands, Great Britain and the United States), there has been stagnation and in some cases small increases or even a large increase in the proportion of alcohol related fatalities, as was the case in Sweden. Trends on drug impaired driving are also beginning to emerge in some countries. These trends will also be discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".