Bibliographic record
Abstract
Alcohol-impaired driving is a major threat to traffic safety. Considerable progress has been made in recent decades in countries throughout the industrialized world. In the last several years, however, this progress has stalled, and in some countries, progress has eroded. In the United States, in 2005 almost 17,000 people died in alcohol-related crashes. That number has been virtually unchanged for the past decade. Well-known and effective approaches to impaired driving involve enforcement and deterrence to keep drinkers from driving. Another set of promising strategies attempt to reduce alcohol consumption through regulation of the sale and service of alcohol and thus make it less likely that potential drivers will drink enough to be impaired. The most well-known alcohol regulation that has made a major contribution to traffic safety has been the establishment of 21 as the drinking age throughout the United States. By reducing alcohol consumption among immature and inexperienced drivers, tens of thousands of traffic fatalities have been prevented. Other alcohol regulatory strategies can make alcohol more expensive or reduce its availability in risky situations. In order to provide a systematic review and synthesis of the many regulatory strategies that have been implemented and evaluated, the Transportation Research Board’s Alcohol, Other Drugs, and Transportation Committee convened a workshop to discuss the role of alcohol regulation in traffic safety. The workshop was held at the National Academies’ Beckman Conference Center in Irvine, California, June 5–6, 2006. Attendees came from five different countries: Canada, Sweden, New Zealand, Mexico, and the United States. This report provides an overview and summary of the information presented, the discussions among the participants, and the background papers prepared for the workshop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".