If you’re high status and you know it: Teasing apart the within- and between-person effects of peer- and self-reported status in the drinking group on alcohol-related outcomes.
Bibliographic record
Abstract
In this longitudinal study, we disentangled within- and between-persons effects in the relationship between university students' status in their drinking group and alcohol-related behavior. We further examined the role of self-perceived and peer-reported status, with the hypothesis that only when students' peers reported them as of a higher status, and they were aware of their high status (via self-report), would they experience increased heavy episodic drinking (HED). University students (N = 118; Mage = 19.40, SD = 1.49; 60.2% women) were recruited in their natural drinking groups (N = 27). All group members completed surveys at 3 time points during the school year, each 2 months apart. We fitted a taxonomy of multilevel growth curve models predicting students' self-reported HED and the extent to which they encouraged other group members to consume alcohol (peer-reported). Between-persons results demonstrated that students who reported higher status compared to their group members experienced more HED on average and students who were peer-reported as of a higher status relative to their group members played a more salient role in encouraging others to drink. Notably, and consistent with hypotheses, a within-person interaction revealed that at time points when students were higher in peer-reported status relative to their average, and they were aware of their increase in status (via self-reports), they also engaged in more HED. Results emphasize the importance of considering within-person effects and highlight the need for university alcohol-prevention programming to focus on students' status-related motives and concerns. (PsycINFO Database Record
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".