Young Impaired Driver Involvement in Fatal Crashes
Bibliographic record
Abstract
Elder and Shults (2002) reported that between 1982 and 2001 the rate of drinking drivers in fatal crashes per 100,000 population in all age groups declined by 46%. In contrast, the rate for teenagers (15 to 17 years) declined 60% and for youths (18 to 20 years), 55%. Various explanations have been suggested for the reduction in alcohol-related crashes over the last quarter century. A study by Dang (2008), recently published by the National Highway Traffic Safety Administration (NHTSA), found evidence that two factors were of major importance: (1) demographic changes including the aging of the population and a decline in the proportion of licensed drivers who are males, and (2) the passage of several impaired-driving laws, particularly the minimum legal drinking age (MLDA) law. Because of the evidence that drivers younger than 21 are at higher risk for involvement in crashes, particularly alcohol-related crashes, the contribution of that age group to the observed reductions in alcohol-related crashes is of special interest. Given the large number of laws and programs initiated during the last quarter century that affect drivers of all ages, there is interest in determining whether youth for whom special “status” laws were enacted benefited more than older drivers did. This topic is relevant to the current controversy over the benefits of the MLDA law. The Dang study found that the MLDA law reduced by 40% the fatal crash involvements of underage drivers with blood alcohol concentration (BAC) of .08 or higher. The objective of this study is to update the Elder and Shults (2002) study. Because of the complex factors that influence alcohol-related crashes, this report considers several methods, other than population, for calculating crash rates to measure the trend for underage drinking drivers in alcohol-related crashes between 1982 and 2004.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".