A new alcohol-related traffic law, a further reduction in traffic fatalities? Analyzing the case of Turkey
Bibliographic record
Abstract
BACKGROUND: In June 2013, an alcohol-related traffic law took effect in Turkey. The law 6487 introduced administrative fines for not respecting blood alcohol concentration limits, health warning messages on alcohol containers (bottles, cans), and prohibited the sale of alcohol beverages in retail facilities between 10 p.m. and 6 a.m.. This article examines how this law is associated with traffic fatality variation. METHODS: Data from the Turkish Statistical Institute for the 2008-2019 period were analyzed. Outcomes were traffic fatality rates per 100,000 population and 10,000 motor vehicles. Exposure variable was the presence of law 6487. Alcohol, tobacco, and related beverages' household expenditure, unemployment rate, number of health professionals, number of crashes, and lags of the outcomes represented control variables. A time-series cross-regional fixed effect model was applied. RESULTS: Empirical estimates suggest that the law 6487 was associated with a reduction of 15% (Incidence Rate Ratio (IRR) 0.85, 95% Confidence Interval (CI): 082, 0.94) in the traffic fatality per population rate and with a reduction of 14% (IRR: 0.86 (95% CI: 0.78, 0.92) in the traffic fatality per motor-vehicle rate. After 6 years of its implementation, this intervention was associated with an absolute reduction of 1519 (95% reduction interval: 1177, 1810) traffic fatalities. CONCLUSIONS: Our research emphasizes that legislation with direct and indirect measures targeting driving under the influence of alcohol (DUIA) may be related to traffic fatalities reduction. PRACTICAL APPLICATIONS: This finding has important implications for policy and future research in contexts in which alcohol consumption is low such is in Turkey. Future research should seek to identify mechanisms that explain how laws are ultimately associated with DUIA variation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".