Sex and Gender Diversity Among Transgender Persons in Ontario, Canada: Results From a Respondent-Driven Sampling Survey
Bibliographic record
Abstract
Recent estimates suggest that as many as 1 in 200 adults may be trans (transgender, transsexual, or transitioned). Knowledge about dimensions of sex and gender in trans populations is crucial to development of inclusive policy, practice, and research, but limited data have been available, particularly from probability samples. The Trans PULSE community-based research project surveyed trans Ontarians (n=433) in 2009-2010 using respondent-driven sampling. Frequencies were weighted by recruitment probability to produce estimates for the networked Ontario trans population. An estimated 30% of trans Ontarians were living their day-to-day lives in their birth gender, and 23% were living in their felt gender with no medical intervention. In all, 42% were using hormones, while 15% of male-to-female spectrum persons had undergone vaginoplasty and 0.4% of female-to-male spectrum persons had had phalloplasty. Of those living in their felt gender, 59% had begun to do so within the past four years. A minority of trans Ontarians reported a linear transition from one sex to another, yet such a trajectory is often assumed to be the norm. Accounting for this observed diversity, we recommend policy and practice changes to increase social inclusion and service access for trans persons, regardless of transition status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".