A Comparison of Advertising Policies for Cannabis, Alcohol, and Gambling: The Case of Ontario, Canada
Bibliographic record
Abstract
When attempting to draft policies, legislators and regulators often look to other jurisdictions for inspiration. Similarly, when aiming to benchmark policies, comparisons are frequently made against other jurisdictions. When cannabis was legalized for recreational use in Canada in 2018, many of the same entities that were responsible for regulating alcohol and gambling became responsible for also regulating the sale of cannabis. However, the laws and local policies governing the availability and promotion of these substances/activities vary considerably. We suggest that comparing regulatory policies associated with gambling, alcohol, and cannabis will provide unique insights that may inform approaches to gambling regulation and help to identify novel areas for improvement. This session will provide a case study and examples, considering how availability and advertising policies for cannabis, alcohol, and gambling differ within the same jurisdiction. The discussion will focus on lessons that can be applied to the gambling field from this exercise, with respect to policy approaches, prevention and harm reduction programming, and evaluation.\nImplications:\nThis session will provide attendees with guiding questions and an approach for reframing assessment of gambling policies and programs by considering alignment with related fields such as cannabis and alcohol, as well as encouraging attendees to consider how their gambling-focused work might overlap with policy or programming in related fields.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".