Beware of His Car: Why Are Men More Dangerous than Women Behind the Wheel?
Bibliographic record
Abstract
According to statistics, men in Russia and most countries of the world are significantly more likely to cause road accidents than women. Understanding the reasons for these differences may be important for developing measures to reduce the number of road accidents. In the literature on Russia, the issue of the causes of this gender gap remains understudied. We analyse the magnitude of the gap in the odds of committing a serious crash by drivers of different genders and discuss possible reasons for these differences. For this, we use a dataset of 158,000 published court decisions under Article 264 of the Russian Criminal Code for the period from 2010 to 2022. We show that 91.7% of all cases involve male drivers. But even after accounting for differences in the number of drivers of different genders and the number of kilometers they drive on average, men are 3.25 times more likely to commit crashes resulting in criminal prosecutions. One reason for these differences is driving safety. Men are also more likely to commit aggravated road accidents. In almost a quarter of cases, male drivers were driving drunk, while for women the figure is only 10 percent. Judges in turn are more likely to give men a serious sentence if it is a non-fatal offence; in more serious cases, the gender of the driver is less important. We also found a very strong variation in the odds of causing a serious road accident for men and women between Russian regions, suggesting the influence of cultural and socio-economic differences.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".