Bibliographic record
Abstract
Studies of homonegativity in the general population typically use scales to examine the attitudes of a heterosexual sample toward gay men and lesbian women. However, these scales fail to address that accepting gay and lesbian people in theory is not tantamount to accepting the sexual practices engaged in by gay and lesbian people. As a result, relying on homonegativity scales and hypothetical scenarios (i.e., asking a participant to imagine a gay man or lesbian woman from personality characteristics provided) may not offer a complete view of the complexities of homonegativity. To explore this possibility, 83 men self-identifying as either largely or exclusively heterosexual rated one of three groups of images (romantic gay, erotic gay, and control) on the basis of five questions related to their emotional responses. A psychometrically sound homonegativity scale was also completed. Results indicated that homonegativity was a significant predictor of decreased happiness, anger, disgust, task enjoyment, and reported liking of the imagery. Furthermore, homonegativity was found to moderate the association between exposure to the romantic images and four of the five emotional responses (happiness, anger, disgust, and liking). Exposure to the set of erotic gay images, however, was associated with negative emotional responses, regardless of participants' self-reported level of homonegativity (i.e., overt homonegativity possessed less moderational power for this type of imagery). These findings suggest that standard scales of homonegative attitudes may be unable to capture the affective negativity that heterosexual men experience when viewing gay male intimacy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".