Playing to Win: Male–Male Sex‐Based Harassment and the Masculinity Contest
Bibliographic record
Abstract
Abstract Workplaces characterized by masculinity contests equate masculinity with status, making it especially critical to prove masculinity and defend against threats to this identity. Past scholarship has explained male–female sex‐based harassment (MF‐SBH) as a strategy for defending threatened masculinity and the gender hierarchy more broadly. The current research examines whether male–male SBH (MM‐SBH) is also triggered by a desire to reassert a threatened sense of masculinity. Specifically, I explore the effects of two forms of masculinity threat on men's propensity to harass another man: prototypicality threat (suggesting one is gender atypical) and distinctiveness threat (suggesting the sexes are more similar than they are different). An online experiment and a lab study indicated that prototypicality threat, but not distinctiveness threat, leads to greater MM‐SBH. I suggest that masculinity contest workplaces, which especially highly prize masculinity, likely exacerbate this effect.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".