Accuracy and Bias in Perceptions of why Social Network Members Drink: A Truth and Bias Approach to Drinking Motive (mis)perception
Bibliographic record
Abstract
Perceived drinking motives of social network members appear to influence emerging adults’ alcohol use indirectly through their own drinking motives. Ascertaining the accuracy of motive perceptions can determine the relevance of social norm interventions for drinking motives and the utility of egocentric versus direct-reporting social network designs. As part of a larger study, 60 emerging adults (70% female; mean age = 21.57) reported cross-sectionally on their own drinking motives and the drinking motives of a peer. Peers were recruited and reported on their drinking motives. Regression analyses utilizing the truth and bias model indicated social, coping-with-anxiety, and coping-with-depression motives exhibited accuracy. Participants also overestimated peers’ social, enhancement, and conformity motives. Coping-with-depression and enhancement motives exhibited assumed similarity. Most motive perceptions were heavily or singularly influenced by bias. Whether to include actual and/or perceived motives in social network research designs should be carefully considered.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".