The social media response to the rollout of legalized cannabis retail in Ontario, Canada
Bibliographic record
Abstract
With Canada becoming the first G20 country to legalize the recreational use of cannabis, there has been increasing interest in the emergence of this new retail market. The research utilizes social media analytics to analyze the public's response to the rollout of the government-controlled cannabis retail stores: Ontario Cannabis Store (OCS). The research analyzes 17,162 tweets mentioning the OCS (@ONCannabisStore) on Twitter in the one-year period following the legalization of recreational cannabis. Using thematic analysis, 19 codes are identified and further categorized under six themes—i.e., consignment, product, retail model, policy, producers, and consumers. The research provides valuable insight into the public's perceptions of the newly legalized cannabis retail market on social media. As a practical implication of the research, key concerns and issues with the initial retail rollout are identified, which provides insight into the evolution of an illegal to legal retail market. The methods can be used by future researchers, policy makers, and emerging cannabis retailers to gather and understand cannabis consumers' opinions on social media. Furthermore, the findings can be leveraged to inform future government policies and decisions around the emergence of this new retail sector.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".