Beer, wine and distilled spirits in Ontario: A comparison of recent policies, regulations and practices
Bibliographic record
Abstract
Aims There is a long-standing discussion about whether some beverages are more likely to be linked with high-risk drinking and damage than others, and implications for beverage specific alcohol policies. While the evidence is inconclusive, when controlling for individual consumption, some studies have shown elevated risks by beverage type. This paper examines the situation in Ontario, Canada, from 1995 to present (2011) on several dimensions in order to assess the differences by beverage and their rationale with a specific focus on the most recent policie. Methods This paper draws on archival consumption statistics, taxation and pricing arrangements, and retailing and marketing practices. Results Off-premise sales, which represent an estimated 75% of ethanol, involve several channels: stores controlled by the Liquor Control Board (LCBO) – which sell all spirits, imported and domestic wines, and beer products; the Beer Store network which sell all beers; and Ontario winery stores – which sell Ontario wines. In LCBO stores Ontario wines are more prominently displayed than other beverages, and extensive print advertising tends to feature wine over beer and spirits. There are also differences by beverage in terms of taxation and price. The taxes on higher alcohol content beverage types account for a higher portion of the retail price than taxes on lower alcohol content beverage types. Furthermore, minimum price regulations allow for differential minimum pricing per standard drink [17.05 ml ethanol] across beverage types. Conclusions The apparent rationale for these arrangements is not primarily that of favouring lighter-strength beverages in order to reduce harm, but rather to accommodate long-standing vested interests which are primarily financially based.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".