Gender and Sexuality Disparities in Perception, Attitude and Social Intimacy Among Sinophone Youth Toward Transgender and Gender Non-Conforming Individuals: Based on an Internet Survey
Bibliographic record
Abstract
The study aims at assessing gender and sexuality characteristics (GSC) in perception, attitude, and social intimacy among Sinophone youth toward transgender and gender non-conforming (TGNC) people. Based on an internet survey with 3 825 valid questionnaires, we distinguished the general public into TGNC, cisgender heterosexual, and cisgender non-heterosexual individuals. Then we classified TGNC individuals into trans females, trans males, and non-binary/genderqueer people and cisgender individuals into cisgender females and cisgender males. The chi-square test, one-way analysis of variance (ANOVA), and multiple linear regression were used. We found that the evident gender and sexuality disparities in perception, attitude, and social intimacy toward TGNC individuals exist both in and out of TGNC individuals. Negative perceptions and attitudes as well as alienated social intimacy were most pronounced among cisgender heterosexual people (Chi-square test, one-way ANOVA, and multiple linear regression: all p < .001). Cisgender females exhibit higher levels of supportiveness compared to cisgender males. Trans females were the most positive while they also had more concerns regarding public space and gender expression-related issues. The findings are practical for community-based advocacy for raising public awareness of the presences and experiences of TGNC people in Sinophone societies.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".