Femmephobia Is a Uniquely Powerful Predictor of Anti-Gay Behavior
Bibliographic record
Abstract
The current study explored a form of femmephobia (specifically, negative attitudes toward femininity in men) as a predictor of anti-gay behaviors among a sample of heterosexual men (N = 417). Additional predictor variables included hierarchical worldviews (i.e., social dominance orientation, right-wing authoritarianism, narcissism) and prejudicial attitudes (i.e., old-fashioned and modern homonegativity). Femmephobia emerged as a robust predictor, accounting for 23% of the variance in anti-gay behavior, surpassing the explanatory power (15%) of all other considered variables combined. Moreover, social dominance only predicted anti-gay behavior when femmephobia levels were high. Future research on discrimination and violence related to sexual identity and gender expression should incorporate femmephobia as a key predictive factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".