Bibliographic record
Abstract
Preventing impaired driving around the world: lessons learnedThe last two decades have brought dramatic progress in reducing alcohol related crashes in most of the developed world.Declines have occurred in Canada, Australia, New Zealand, most Western European countries and in the United States.The United States has seen declines both in the number of alcohol related traYc fatalities and in the proportion of traYc fatalities that are alcohol related.Many diVerent factors have contributed to the progress we have seen and certainly diVerent factors have been predominant in diVerent countries.Articles in this issue of the journal provide information on some of the activities in the United States and Canada.In the United States, we tend to assume that we are the center of the universe and that almost anything of significance is invented here.Prevention of impaired driving, however, is one area where we acknowledge our debt to other countries that have pioneered many of the most eVective prevention strategies.For example, the United States drew valuable lessons regarding deterrence from analyzing the results of the British Road Safety Act of 1967.Similarly, we have learned about alcohol policy and serious enforcement and penalties from some of the Scandinavian countries.The Australian experience with random breath testing has influenced some of our own enforcement eVorts.The US National Highway TraYc Safety Administration recently sponsored a systematic eVort to gather information about impaired driving laws from countries around the world.The intent of this eVort is to contribute to our understanding of impaired driving countermeasures and of how the current situation in the United States compares to other countries.The project also includes an analysis of alcohol involvement in fatal traYc crashes in countries around the world and the relevant regulations, definitions, and procedures used to measure and report alcohol involvement.The primary purpose of this project is to provide comparisons with the United States, and therefore possible guidance in the development and implementation of impaired driving policies in this country.Therefore, the main focus of data collection is on countries that would be considered most directly comparable to the United States economically and demographically.The key comparison countries include members of the European Union, other Western European countries, Canada, Australia, and New Zealand.The results of this study, which is still ongoing, indicate how much we have shared from country to country in improving the laws, policies, and practices related to impaired driving.The study also indicates some of the major diVerences in approach that remain.Some highlights of the findings thus far are summarized below.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".