Media reporting of traffic legislation changes in British Columbia (2010)
Bibliographic record
Abstract
INTRODUCTION: In 2010, British Columbia (BC) introduced new traffic laws designed to deter impaired driving, speeding, and distracted driving. These laws generated significant media attention and were associated with reductions in fatal crashes and in ambulance calls and hospital admissions for road trauma. OBJECTIVE: To understand the extent and type of media coverage of the new traffic laws and to identify how the laws were framed by the media. METHODS: We reviewed a database of injury related news coverage (May 2010-December 2012) and extracted reports that mentioned distracted driving, impaired driving, or speeding. Articles were classified according to: (i) Type, (ii) Issue discussed, (iii) 'Reference to new laws', and (iv) 'Pro/anti traffic law'. Articles mentioning the new laws were reread and common themes in how the laws were framed were identified and discussed. RESULTS: Over the course of the study, 1848 articles mentioned distraction, impairment, or speeding and 597 reports mentioned the new laws: 65 against, 227 neutral, and 305 supportive. Reports against the new laws framed them as unfair or as causing economic damage to the entertainment industry. Reports in favor of the new laws framed them in terms of preventing impaired driving and related trauma or of bringing justice to drinking drivers. Growing evidence of the effectiveness of the new laws generated media support. CONCLUSIONS: BC's new traffic laws generated considerable media attention both pro and con. We believe that this media attention helped inform the public of the new laws and enhanced their deterrent effect.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".