From Storefronts to Headlines: Framing News Media Content to Understand the Barriers to Cannabis Retail Operation in Canada
Bibliographic record
Abstract
Background Since the legalization of cannabis in Canada, there have been numerous issues impacting private cannabis retailers, potentially affecting public health and safety. Media framing of these issues plays a crucial role in shaping public perception and policy discourse. This study examined the media's portrayal of the challenges associated with cannabis legalization and its impact on private cannabis retail operations. Methods News media content and framing analyses of Canadian news articles published between 2017 and 2022 were conducted. Articles were selected from Nexis Uni and Eureka databases if they referenced at least one barrier to private cannabis retail stores. Data were extracted and screened using Covidence and deductively coded to depict how the media presented these barriers, focusing on their definitions, underlying causes, moral reasoning, and suggested solutions. Results Of the 9,371 articles screened, 293 qualified for inclusion. The analysis revealed that media portrayals of cannabis retailers’ barriers varied, with government regulatory challenges, supply chain challenges, and unlicensed market competition as the most prominent frames. While some articles portrayed these challenges as hindrances to business success, the majority depicted them as the government's responsibility, justifying its stance by emphasizing the need to protect public health and safety and eliminate the unlicensed market. Conclusion This study indicates that the way media frame the challenges faced by cannabis retailers has the potential to influence public opinion and shape policy debates. Recognizing these media dynamics is essential for informing the development of effective government policies that not only support a stable cannabis retail market but also prioritize public health and safety.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".