The influence of positional and experienced social benefits on the relationship between peers and alcohol use
Bibliographic record
Abstract
An assumption of peer influence research is that being connected to an alcohol-using peer group is associated with personal alcohol use. However, most research assesses peer influence through simply counting the number of peers involved in a particular behavior or the amount of that behavior within a person’s peer group. Rarely considered is the fact that behavioral pressures may only arise when the peer group actually provides substantial benefits to adolescents. This study examines whether adolescents who associate with peers who drink are as likely to be drinkers themselves when they receive high levels of social benefits as compared to adolescents who receive lower levels. Specifically, the authors examine how the effect of peer alcohol use on individual decisions to drink is conditioned by the social status and power that come with occupying sociometrically optimal positions (high popularity, centrality, density) and more concretely experienced social benefits (e.g. spending time/talking with/confiding in friends). Using the longitudinal Add Health data (n = 13,351), we find that peer alcohol use is most strongly related to personal alcohol use when a person is subject to greater social benefits in terms of both sociometric position and through closer one-on-one interactions with peers.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".