Bibliographic record
Abstract
Dangerous alcohol consumption among university students continues to be a major issue in Canada. Numerous studies, focusing on high-risk alcohol consumers, have explored potential variables to explain this behaviour. Positive deviance (PD) offers an alternative framework, one that looks to members of the at-risk group whom manage to demonstrate behaviours that are more functional and healthy as compared to the more typical 'deviant' behaviour. This study examines whether variables identified in the sexual health and delinquency PD literature (e.g., perceived self-efficacy) would predict responsible alcohol consumption among university students. Three categories of students were surveyed: current alcohol abstainers (n=89), responsible drinkers (n=115), and binge drinkers (n=217) using a convenience sampling strategy at an Atlantic Canadian university. Results from our multinomial logistic regression were supported (X²=246.78, df=18, p<.001), with several of our predictor variables significantly predicting group membership. While the model classification accuracy rate (i.e., 66.0%) exceeded the proportional by chance accuracy rate (i.e., 38.4%), providing further support for the model, the model itself best predicted binge drinker membership over the other two groups. Practice and future research implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".