Under the Influence: How Viewing Extreme Partying and Drinking on Social Media Shapes Group Perceptions
Bibliographic record
Abstract
Social media use is omnipresent among college students. The current study investigated how exposure to student risk-taking forms of alcohol use on social media shapes the perceptions of the prototypical student and drinking norms among students. A 2020, three time-point experiment was conducted that measured 208 (M age = 18.85, SD = 1.94; 160 female) participant's partying/drinking prototypes along with their perceived normative support of alcohol consumption. At Time 2, participants were randomly assigned to one of the four conditions, three video conditions and one non-video condition, with one video condition displaying risk-taking drinking behavior. A Mixed ANOVA revealed that within the risk-taking drinking condition, participants used more pro-alcohol words to describe the typical ingroup member and perceived an increase in normative support of alcohol consumption. Implications of this study suggest that risk-taking content from social media may pose barriers to developing social norms interventions to address problematic college student drinking.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".