Does Red Bull give wings to vodka? Placebo effects of marketing labels on perceived intoxication and risky attitudes and behaviors
Bibliographic record
Abstract
Abstract Why sexual assaults and car accidents are associated with the consumption of alcohol mixed with energy drinks (AMED) is still unclear. In a single study, we show that the label used to describe AMED cocktails can have causal non‐pharmacological effects on consumers' perceived intoxication, attitudes, and behaviors. Young men who consumed a cocktail of fruit juice, vodka, and Red Bull felt more intoxicated, took more risks, were more sexually self‐confident, but intended to wait longer before driving when the cocktail's label emphasized the presence of the energy drink (a “Vodka‐Red Bull cocktail”) compared to when it did not (a “Vodka” or “Exotic fruits” cocktail). Speaking to the process underlying these placebo effects, we found no moderation of experience but a strong interaction with expectations: These effects were stronger for people who believe that energy drinks boost alcohol intoxication and who believe that intoxication increases impulsiveness, reduces sexual inhibition, and weakens reflexes. These findings have implications for understanding marketing placebo effects and for the pressing debate on the regulation of the marketing of energy drinks.
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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 | 0.016 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".