Older Drivers in the News: Killer Headlines v Raising Awareness
Bibliographic record
Abstract
The daily print media continues to be an important political and social influence, shaping opinions and setting agendas. Yet few studies have examined Australian newspaper coverage of older drivers, despite researchers calling for increased public awareness of issues related to the growing number of older drivers on Australian roads. This study analyses the content and discourse of articles on older drivers and issues related to them from 11 Australian metropolitan daily newspapers, representing all state and territory capitals, over three periods: 2010-2014 (inclusive), 2016 and 2017. It focuses on three main areas: the topics covered; keywords, stock phrases and stereotypes used; and attributed sources, including who is quoted and where. Several patterns were apparent from the qualitative and quantitative analysis. Articles appeared sporadically but tended to cluster around reports of serious crashes where at least one driver was aged over 60 years. The debate was focused on age, with calls for testing and compulsory age-based restrictions common but few articles mentioned the contribution of the 'frailty bias' to the over-representation of older people in fatality and serious injury crash statistics. A better understanding of the way newspapers present such issues has much potential to identify and address misperceptions around safe driving and ageing.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".