The Strong Effect of Other People's Drinking: Two Experimental Observational Studies in a Real Bar
Bibliographic record
Abstract
Research has demonstrated that when people are with heavy-drinking peers, they consume more alcohol than when they are in the company of light-drinking peers. This social influence process has usually been investigated in clinical laboratories or seminaturalistic drinking settings such as laboratory bars. The question remains whether these robust effects can be replicated in real-life drinking settings. The aim of these experimental studies was to examine social influence processes in real bars. In Study 1 a two (confederate drank alcoholic vs. nonalcoholic drinks) by two (male vs. female participant) between-participant design was used to test imitation in same-sex dyads (N = 79). Study 2 tested differences in imitation between same- and other-sex dyads with a two (confederate drank alcoholic vs. nonalcoholic drinks) by two (male vs. female confederate) between-participant design (N = 60). Both studies showed that participants consumed more alcohol in the alcohol condition than the nonalcohol condition. No sex differences emerged in the extent to which participants imitated their drinking partners. Study 2 demonstrated no difference in imitation between same-sex and other-sex dyads. Results support the ecological validity of research on imitation of alcohol consumption conducted in laboratory bars.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".