Predicting High School Minority Adolescents’ Drinking from Their Exposure to White Schoolmates: Differences and Similarities among Hispanic, Black, and Asian U.S. Adolescents
Bibliographic record
Abstract
White students' drinking may constitute a risk factor for drinking among same-school minority adolescents. Our study examined data from 14,986 ethnic minority American high school students (56% female, mean age = 15.6). Models examined associations between school-level White student drinking and same-school Black, Hispanic, and Asian adolescents' drinking, as well as whether schools' proportions of White students and friendships with White schoolmates moderated these associations. Both school-level White students' drinking and minority students' friendships with White schoolmates were associated with levels of minority student drinking. But these associations were dependent upon levels of other study variables. In particular, there were higher associations between school-level risk factors and minorities' drinking when minority adolescents had high proportions of Whites among their friends.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".