Prevalence of risky driving behaviours on popular television series
Bibliographic record
Abstract
Summary: Motor vehicle crashes are a leading cause of death among young adults. Social media and television have been shown to affect the likelihood that young adults will engage in risk-taking behaviour. We watched 216 episodes of five popular television series on Netflix and identified 333 separate driving scenes, of which 271 (81.4%) portrayed at least one risky driving behaviour. Unsafe driving (not wearing a seat belt) was the most common risky driving behaviour noted, occurring in 245 (73.6%) of driving scenes. Distracted driving (36 [18.8%]) and driving while using a cellphone (28 [8.4%]) were also noted. Popular television series model unsafe driving behaviours. Seat belts are infrequently used. As well, drivers are often distracted, looking away from the road to talk or talking on their cellphones. Television producers should be sensitive to modelling unsafe driving behaviours, particularly if the audience consists largely of young people.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".