An Examination of Attitudes toward Bisexual People at the Intersections of Gender and Race/Ethnicity
Bibliographic record
Abstract
People report more negative attitudes toward bisexual than gay/lesbian individuals, but little is known about attitudes at the intersections of gender and race/ethnicity. We examined whether attitudes toward bisexual people differed depending on: 1) target gender identity (man, woman), gender modality (cisgender, transgender), and race/ethnicity (White, Black, Hispanic); and 2) participant gender identity (man, woman) and race/ethnicity (White, person of color). As part of a cross-sectional survey, 552 participants rated their feelings toward 12 bisexual targets who varied in gender identity/modality and race/ethnicity. A repeated-measures ANOVA indicated that participants rated bisexual men more negatively than women, transgender individuals more negatively than cisgender individuals, and Black/Hispanic individuals more negatively than White individuals. However, differences based on target gender identity and race/ethnicity were only observed for cisgender targets, and most effects were only observed for male participants. Efforts to improve attitudes toward bisexual people must account for heterogeneity based on target/participant characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".