Impact of lowering the legal blood alcohol concentration limit to 0.03 on male, female and teenage drivers involved alcohol-related crashes in Japan
Bibliographic record
Abstract
In June of 2002, a revision to part of the Road Traffic Act drastically increased the penalties for drinking and driving offences in Japan. Most notably, the legal blood alcohol concentration (BAC) limit for driving was lowered from 0.05 mg/ml to 0.03 mg/ml. The rationale for the new lower BAC limit was predicated on the assumption that drinking drivers will comply with the new, lower limit by reducing the amount of alcohol they consume prior to driving, thereby lowering their risk of crash involvement. This, in turn, would lead to fewer alcohol-related crashes. A key limitation of previous lower BAC evaluation research in determining the effectiveness of lower legal BAC limit policies is the assumption of population homogeneity in responding to the laws. The present analysis is unique in this perspective and focuses on the evaluation of the impact of BAC limit reduction on different segments of the population. The chief objective of this research is to quantify the extent to which lowering the legal limit of BAC has reduced male, female and teenager involvement in motor vehicle crashes in Japan since 2002. Most notably, the introduction of reduced BAC limit legislation resulted in a statistically significant decrease in the number of alcohol-impaired drivers on the road in Japan, indicating responsiveness to the legal change among adults and teenagers. In addition, this preliminary assessment appears to indicate that the implementation of 0.03 BAC laws and other associated activities are associated with statistically significant reductions in alcohol-involved motor vehicle crashes. In comparison, the rates of total crashes showed no statistically significant decline nor increase in the period following the introduction of the BAC law, indicating that the lower BAC limit only had an effect on alcohol-related crashes in Japan. The evidence suggests that the lower BAC legal limit and perceived risk of detection are the two most important factors resulting in a sustained change in drinking and driving behaviour in Japan. It is recommended that future research and resources in other countries be focused on these factors as determinants to reduced alcohol-related crashes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".