Examining the Barriers to Licensed Private Cannabis Retailers in Canada: A Quantitative Content Analysis of Canadian News Media Coverage
Bibliographic record
Abstract
Background Since the Government of Canada legalized non-medicinal cannabis use/consumption on October 17, 2018, licensed private cannabis retailers have faced numerous challenges. These challenges negatively impacted retail operations and potentially undermined public health and safety. We aimed to identify these challenges to inform policy decisions. Methods We conducted a quantitative news media content analysis using Nexis Uni and Eureka databases to identify articles from Canada's highest-circulating newspapers and CBC News website from 2017 to 2022, referencing at least one barrier to private cannabis retailers. We screened and extracted data using Covidence and deductively coded the data using our newly developed comprehensive cannabis retail framework and inductively as new themes emerged. The barriers identified in the media were quantified through descriptive analyses. Results The search yielded 9,371 articles, of which 307 relevant articles were included. The main findings revealed that the barriers most commonly reported by the media were related to government regulations, supply chain, and competitors. The salience of these barriers also changed over time. These barriers were most frequently mentioned in the Globe and Mail, Calgary Herald, Toronto Star, and the National Post. Conclusion Our framework was suitable for describing the data and identifying several media-portrayed barriers to private cannabis retail operations in Canada and how they differed in salience over time. However, a more in-depth understanding of the barriers from retailers’ perspectives may further support the government's policy agenda of achieving public health and safety. The results from this study may also serve as a baseline measure to evaluate Canada's cannabis retail market and provide new insights into the growing body of literature about the cannabis retail market.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.024 | 0.042 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".