Development and Validation of a Scale Measuring Attitudes Toward Non-Drinkers
Bibliographic record
Abstract
The idea that individuals drink alcohol to fit in with their peers has been investigated by many researchers. However, the related concept that consumption of alcohol may serve as a means of avoiding the social costs associated with being a non-drinker has received little attention. Three studies (N = 94, 148, 236) are outlined, which detail the construction and preliminary validation of the Regan Attitudes toward Non-Drinkers Scale (RANDS). Results indicated that scale score reliability for the RANDS was good (α values range from .82 to .89) with exploratory and confirmatory factor analyses suggesting that the scale possesses a unidimensional factor structure. Importantly, scores on the RANDS emerged as a stronger predictor of self-reported yearly alcohol consumption and binge-drinking than indicants commonly assessed in alcohol use and abuse research in adolescents and young adults (e.g., peer pressure). Limitations of these studies and directions for future research are outlined.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".