Bibliographic record
Abstract
A persistent finding in past research reifies a “gendered” cisnormative bias whereby heterosexual men (compared to heterosexual women) have been found to be overwhelmingly less supportive of transgender individuals in quantitative studies conducted in the United States and in Canada, China, Malaysia, the Philippines, Poland, Singapore, Sweden, Thailand, and the United Kingdom. I suggest that this finding reflects a synergistic relationship between “transphobia” and “homophobia” or, put another way, an overarching presence of hetero-cis–normativity whereby it is “normal” to be both heterosexual and cisgender and it is not normal (and therefore acceptable to be prejudiced toward) nonheterosexual and noncisgender individuals. Using this hetero-cisnormative framework in the current study, I utilize quantitative survey data from college-age students (N = 775; average age, 22; 78% White) at a university in the southern United States to investigate attitudes toward transgender individuals in three ways. First, I explore how hetero-cis–normative assumptions lead to gender differences in attitudes toward male-to-female and female-to-male transgender individuals. Next, I examine perspectives in opposition to hetero-cis–normativity—namely feminist identity and supportive attitudes toward lesbian, gay, and bisexual individuals—to explain why men (compared to women) have more negative attitudes toward transgender individuals. Finally, I explore how nonheterosexuals' attitudes may further elucidate the relationship between gender and attitudes toward transgender individuals. Overall results provide support for using a hetero-cis–normative framework to understand transphobia.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".