Documenting Research with Transgender, Nonbinary, and Other Gender Diverse (Trans) Individuals and Communities: Introducing the Global Trans Research Evidence Map
Bibliographic record
Abstract
There is limited information about how transgender, nonbinary, and other gender diverse (trans) people have been studied and represented by researchers. The objectives of this study were to: (1) increase access to trans research; (2) map and describe trans research across subject fields; and (3) identify evidence gaps and opportunities for more responsible research. Eligibility criteria were established to include empirical research of any design, which included trans participants or their personal information and that was published in English in peer-reviewed journals. A search of 15 academic databases resulted in 25,230 references; data presented include 690 trans-focused articles that met the screening criteria and were published between 2010 and 2014. The 10 topics studied most frequently were: (1) therapeutics and surgeries; (2) gender identity and expression; (3) mental health; (4) biology and physiology; (5) discrimination and marginalization; (6) physical health; (7) sexual health, HIV, and sexually transmitted infections; (8) health and mental health services; (9) social support, relationships, and families; and (10) resilience, well-being, and quality of life. This map also highlights the relatively minor attention that has been paid to a number of study topics, including ethnicity, culture, race, and racialization; housing; income; employment; and space and place. Results of this review have the potential to increase awareness of existing trans research, to characterize evidence gaps, and to inform strategic research prioritization. With this information, it is more likely that trans communities and allies will be in a position to benefit from existing research and to hold researchers accountable.
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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 | 0.121 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.047 | 0.036 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".