Alcohol, masculinity, honour and male barroom aggression in an <scp>A</scp>ustralian sample
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The link between alcohol and men's aggression is well established, although growing evidence also points to individual and learned social factors. The aim of the present study was to investigate the relationships between male alcohol-related aggression (MARA) among young Australian men and heavy episodic drinking, trait aggression, masculinity, concerns about social honour and expected positive consequences of MARA. DESIGN AND METHODS: The total sample comprised 170 men aged 18-25 years who completed an online questionnaire exploring beliefs and attitudes towards MARA. RESULTS: Those who reported heavy episodic drinking were more likely to be involved in an incident of MARA. In addition, those who were involved in MARA had higher levels of trait aggression, concern for social honour and expected positive consequences of aggression in bars than did those without such involvement. The relationship between socially constructed masculinity factors (a combined variable reflecting masculinity, social honour and expected positive consequences) and MARA was mediated by heavy episodic drinking. Social honour accounted for almost all of the predictive power of masculinity factors. Heavy episodic drinking and trait aggression remained significant predictors of MARA in a multivariate model. DISCUSSION AND CONCLUSIONS: The findings from the current study may assist in developing preventative techniques for young men which target masculinity concerns and the consequences of participating in MARA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".