Somewhere under the rainbow: Exploring the identities and experiences of trans persons
Bibliographic record
Abstract
The literature on transgender/transsexual-spectrum persons is limited. Most studies are based on the assumption that trans persons are best understood within rigid and binary definitions of gender and sexuality and tend to focus on diagnostics, medical management and risk factors. Researchers and clinicians may also assume that people who challenge cultural norms of gender and sexuality can be grouped together. Such assumptions about the specific experiences of trans persons can be harmfully incorrect. The goals of the present study were to explore the gender and sexual identities of trans persons, to investigate group differences, and to examine factors that predict better psychological and physical well-being. Participants took part in an online study and provided information about their gender and sexual identity, social support, relationship quality, and mental/physical health. Results depicted diverse gender identities and sexual orientations among trans persons and emphasized that while many challenges faced by sexual and gender minorities are similar, trans persons report unique mental and physical health outcomes. Also, greater social support and relationship quality predicted mental, but not physical, health among trans persons. These results highlight the importance of acknowledging the complexity of trans identities and the key role of social and personal support.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".