Tackling bisexual erasure: An explorative comparison of bisexual, gay and straight cisgender men’s body image
Bibliographic record
Abstract
Previous body image research often grouped both gay and bisexual men into a single category: sexual minoritised men, limiting our understanding of how sexual identity influences body image. However, there is strong reason to believe that bisexual and gay men experience distinct body image concerns. Here, we explored motivations to alter one’s leanness and muscularity, as well as (dis)satisfaction with body fat, muscularity, height and penis size, and functionality appreciation across gay, bisexual, and straight cisgender men. We sampled 378 white participants aged 18 to 85 (nbisexual = 125, ngay = 128, nstraight = 125). We found that bisexual men were significantly less motivated to be lean and showed lower muscularity dissatisfaction relative to gay men but showed comparable levels to straight men. Our findings demonstrate that despite research perceiving the body image of bisexual and gay men as homogenous, they experience differences in their body image concerning leanness and muscularity dissatisfaction. Future body image research should incorporate this understanding by not artificially grouping bisexual and gay cisgender men and instead acknowledging the potential uniqueness in their experiences.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".