Alcohol Warning Label Perceptions: Do Warning Sizes and Plain Packaging Matter?
Bibliographic record
Abstract
OBJECTIVE: There is a dearth of research on the effectiveness of stringent alcohol warning labels. Our experiment tested whether increasing the size of an alcohol health warning lowers product-based ratings. We examined whether plain packaging lowers ratings of alcohol products and the consumers who use them, increases ratings of bottle "boringness," and enhances warning recognition compared with branded packaging. METHOD: A total of 440 adults (51.7% female) viewed one of three warning sizes (50%, 75%, or 90% of label surface) on either a plain or branded bottle of distilled spirits, wine, and beer. Participants also rated alcohol bottles on product-based (assessing the product itself), consumer-based (assessing perceptions of consumers of the product), and bottle boringness ratings, and then attempted to recognize the correct warning out of four choices. RESULTS: As expected, the size of warning labels lowered product-based ratings. Similarly, plain packaging lowered product-based and consumer-based ratings and increased bottle boringness but only for wine bottles. Further, plain packaging increased the odds of warning recognition on bottles of distilled spirits. CONCLUSIONS: This study shows that plain packaging and warning size (similar to the graphic warnings on cigarette packages) affect perceptions about alcohol bottles. It also shows that plain packaging increases the likelihood for correct health warning recognition, which builds the case for alcohol warning and packaging research and policy.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".