Transgender exclusion from the world of dating: Patterns of acceptance and rejection of hypothetical trans dating partners as a function of sexual and gender identity
Bibliographic record
Abstract
The current study sought to describe the demographic characteristics of individuals who are willing to consider a transgender individual as a potential dating partner. Participants ( N = 958) from a larger study on relationship decision-making processes were asked to select all potential genders that they would consider dating if ever seeking a future romantic partner. The options provided included cisgender men, cisgender women, trans men, trans women, and genderqueer individuals. Across a sample of heterosexual, lesbian, gay, bisexual, queer, and trans individuals, 87.5% indicated that they would not consider dating a trans person, with cisgender heterosexual men and women being most likely to exclude trans persons from their potential dating pool. Individuals identifying as bisexual, queer, trans, or non-binary were most likely to indicate a willingness to date a trans person. However, even among those willing to date trans persons, a pattern of masculine privileging and transfeminine exclusion appeared, such that participants were disproportionately willing to date trans men, but not trans women, even if doing so was counter to their self-identified sexual and gender identity (e.g., a lesbian dating a trans man but not a trans woman). The results are discussed within the context of the implications for trans persons seeking romantic relationships and the pervasiveness of cisgenderism and transmisogyny.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".