Unveiling the Gender Gap in Alcohol Misuse: Fear, Anger, and the “Game of Secrets”
Bibliographic record
Abstract
Gender scholars often assume that alcohol misuse is representative of hegemonic masculinity, but women are simultaneously consuming rates and quantities that parallel those of men. To focus on the gender dynamics of alcohol misuse, the author interviewed 16 members of a metropolitan 12-step recovery community. The findings show how persistent marginalization and trauma contribute to two painful emotions, fear and anger, that influence doing gender through alcohol use. As women and men come to anticipate rejection or harm, they develop a fear of social interactions yet resist painful experiences in divergent ways. Women used alcohol (1) to carve out social mobility and status in spaces previously closed to them; (2) to manage social anxiety, fear, and anger rooted in trauma and marginalization; and (3) to challenge or reshape expectations of femininity by forming hybrid femininities of high expectations. Men used alcohol (1) to accept roles of low status; (2) to manage fear, anger, and emotional suppression tied to past trauma and marginalization; and (3) to construct hybrid masculinities of low expectations that resisted the hegemonic ideals they were socialized into. These findings challenge foundational gender research that links heavy consumption to hegemonic masculinity and inform alcohol research and public health policy.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".