Media reporting on cannabis-impaired driving and related traffic policy in Canada
Bibliographic record
Abstract
In 2018, Canada legalized recreational use of cannabis and introduced new traffic laws to deter impaired driving. These laws received a significant amount of media coverage during legalization. News media framing of driving after cannabis use (DACU) and related traffic policy can influence public opinion on these issues. To understand how DACU and related traffic policy is constructed in Canadian media, examine whether this has changed over time since legalization, and describe and contrast media representation in British Columbia and Ontario. A database of Canadian news content (Canadian Newsstream) was searched for reports published between January 2017 and December 2021. A total of 261 media reports with a focus on DACU published in English in British Columbia and Ontario were selected. Reports were analyzed using content and thematic analyses. The majority of reports depicted DACU as dangerous and legal changes were typically framed in terms of preventing impaired driving. Concerns were frequently expressed over the reliability and accuracy of roadside oral fluid testing and police readiness to detect impaired drivers. Media description of the effects of cannabis legalization on DACU became more positive after legalization. Media portrayal did not differ markedly between British Columbia and Ontario. Media coverage of the new laws may have enhanced their deterrent effect by informing the public about safety risks and legal repercussions associated with DACU. However, mixed messages about law enforcements’ ability to detect and punish impaired drivers may have encouraged DACU by signalling the uncertainty of punishment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".