The Effect of Culture on Gender Differences in Driver Risk Behavior through Comparative Analysis of 32 Countries
Bibliographic record
Abstract
The purpose of the paper is to study the effect of culture on gender differences in accidental risk behaviours. The ESRA2018 database, comprising 25,459 drivers (53% male) surveyed by an online questionnaire in 32 countries, was used to observe gender and regional differences (Africa5, AsiaOceania5, Europa20, NorthAmerica2) in reported behaviour, personal and social acceptability of 4 violations: drinking and driving, speeding, not wearing a seatbelt and phone while driving. The results show that gender differences are small but significant and vary across cultures with men valuing accidental risk behaviours more than women do in all regions observed. In addition, speeding appears to be the most widespread and globally accepted of the four violations tested for both men and women. Results are discussed in terms of origin of gender differences and of factors explaining lack of compliance with speed limitation. Theses results could be useful to better tailor road safety campaigns and education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".