Masculinities, ‘guy talk’ and ‘manning up’: a discourse analysis of how young men talk about sexual health
Bibliographic record
Abstract
Sexually transmitted infection testing rates among young men remain low, and their disengagement from sexual health services has been linked to enactments of masculinity that prohibit or truncate discussions of sexual health. Understanding how men align with multiple masculinities is therefore important for tailoring interventions that appropriately respond to their needs. We draw on 32 in-depth interviews with 15-24-year-old men to explore the discourses that facilitate or shut down sexual health communication with peers and sex partners. We employ a critical discourse analysis to explore how men's conversations about sexual health are constituted by masculine hierarchies (such as the ways in which masculinities influence men's ability to construct or challenge and contest dominant discourses about sexual health). Men's conversations about sexual health focused primarily around their sexual encounters - something frequently referred to as 'guy talk'. Also described were situations whereby participants employed a discourse of 'manning up' to (i) exert power over others with disregard for potential repercussions and (ii) deploy power to affirm and reify their own hyper-masculine identities, while using their personal (masculine) power to help others (who are subordinate in the social ordering of men). By better understanding how masculine discourses are employed by men, their sexual health needs can be advanced.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| 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".