Toward Understanding On-Road Interactions of Male and Female Drivers
Bibliographic record
Abstract
OBJECTIVE: This study examined gender effects in six geometric scenarios of 2-vehicle crashes in which an involved driver could potentially ascertain the gender of the other driver prior to the crash. METHOD: The actual frequencies of different combinations of the involved male and female drivers in these crash scenarios were compared with the expected frequencies if there were no gender interactions. The expected frequencies were based on annual distance driven for personal travel by male and female drivers. RESULTS: The results indicate that in certain crash scenarios, male-to-male crashes tend to be underrepresented and female-to-female crashes tend to be overrepresented. CONCLUSIONS: The obtained pattern of results could be due to either differential gender exposure to the different scenarios, differential gender capabilities to handle specific scenarios, or differential gender expectations of actions by other drivers based on their gender. The current lack of information on gender exposure in different scenarios, scenario-specific driver skills, and driver expectations based on other drivers' gender prevents ruling out any of these possible explanations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".