An Exploratory Comparison and Evaluation of Two Two-Step Measures to Identify Transgender People in Survey Datasets
Bibliographic record
Abstract
Purpose:This study compares and evaluates two distinct two-step approaches to identifying transgender people in survey datasets. Traditional two-step methods using sex assigned at birth (SAB) and current gender identity remain dominant. However, they have notable limitations. Gender modality, or the relationship between SAB and current gender identity (e.g., cisgender, transgender, or something else), presents an important alternative item to consider. Methods:Using an online, cross-sectional survey of 952 sexual and gender minority adults in the United States, we conducted an exploratory analysis of categorization divergence/convergence using two approaches: (1) a modified traditional two-step (SAB + current gender identity) and (2) an alternative two-step (current gender identity + modality). Results:Convergence between approaches was 95%. Rates of refusal for all questions were low, although slightly higher for gender modality. Divergence fell into three categories: (1) individuals grouped as “Questioning” by Approach #2, but not #1 (n=21; 44.7% of divergences); (2) individuals categorizable by one approach, but not the other (n=13; 27.6% of divergences); and (3) individuals whose gender modality differed between approaches (n=13; 27.6% of divergences). Conclusions:We found preliminary evidence for the utility of an alternative two-step approach, particularly when within-group differences among transgender populations are relevant. Both the traditional two-step model and the alternative we tested have limitations which should be ameliorated through future research. Cognitive testing is necessary to evaluate explanations of divergences. We identify priorities to expand on the relative strengths of our alternative approach and address the remaining limitations and areas of uncertainty it highlights.
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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 | 0.207 | 0.372 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".