Red flags on pinkwashed drinks: contradictions and dangers in marketing alcohol to prevent cancer
Bibliographic record
Abstract
AIMS: To document alcohol products and promotions that use the pink ribbon symbol and related marketing materials that associate alcohol brands with breast cancer charities, awareness and survivors. METHODS: We conducted a basic Boolean public internet search for alcohol products with pink ribbon/breast cancer awareness marketing campaigns. RESULTS: There is strong and growing evidence of alcohol as a contributing cause of several types of cancer, including breast cancer. There is no U-shaped curve for cancer, and threshold of elevated relative risk is as low as one drink a day for certain cancers. We found 17 examples of alcohol product campaigns with websites, press releases and social media posts, along with news articles and blog posts from industry and non-profit organizations regarding alcohol products associated with breast cancer causes and charities. Various cancer charities have entered into alliances with sectors of the alcohol industry that raise funds for breast cancer research, treatment or prevention by promoting the purchase of certain alcoholic beverages. CONCLUSIONS: Some alcohol corporations use pink ribbons and other breast cancer-related images, messages and user-generated media to market a product that contributes to cancer disease and death. Therefore, cancer charities should adopt policies to separate them from alliances with the alcohol industry.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".