Barroom Aggression Among Australian Men: Associations With Heavy Episodic Drinking, Conformity to Masculine Norms, and Personal and Perceived Peer Approval of Barroom Aggression
Bibliographic record
Abstract
OBJECTIVE: Research suggests that heavy episodic drinking (HED), perceived peer norms, and personal approval of aggression influence male barroom aggression (MBA). Qualitative research suggests that conformity to hegemonic masculine gender norms also influences MBA; however, quantitative research on the direct and indirect influence of masculinity on MBA is limited. This study tested the relationships between HED, conformity to masculine gender norms, and personal approval and peer approval of MBA on MBA perpetration, as well as the indirect effect of masculine norms on MBA via HED. METHOD: A convenience sample of Australian men (N = 322; mean age = 21.05 years, SD = 1.95; 76.9% university students) completed an online questionnaire, assessing HED and MBA over the previous year, and subscales of the Beliefs and Attitudes Towards Male Alcohol-Related Aggression Inventory and Conformity to Masculine Norms Inventory-46. RESULTS: Negative binomial regression analyses found that, overall, HED, male peer approval, and personal approval of MBA directly predicted increased risk of verbal and physical MBA perpetration. Greater conformity to specific masculine norms also increased (Power Over Women) and decreased (Emotional Control, Heterosexual Self-Presentation) risk of MBA perpetration. The masculine norms Risk Taking, Playboy, and Emotional Control were found to be indirect predictors of MBA via HED. CONCLUSIONS: Risk of MBA perpetration is increased primarily by HED as a direct, but also mediating, predictor. Personal and male peer approval of MBA, and specific masculine norms, further increase this risk whereas other masculine norms appear protective.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".