Gender/sex markers, bio/logics, and U.S. identity documents
Bibliographic record
Abstract
Policies about changing gender/sex on identity documents provide insights into definitions of gender/sex, and impact especially transgender and/or nonbinary lives. We investigated these on U.S. driver's licenses and birth certificates to understand variability in these policies, including in comparison to an earlier report in 2014, and to explore what kinds of “bio/logics” (decision rules rooted in biological or biologistic thinking) might be at play. Results show that the most common requirements in 2020 included proof of gender affirming surgery, a letter from a medical doctor, and hormone therapy. Compared to 2014, results showed an increase in requirements for hormone therapy and letters from therapists or medical doctors, and a decrease in requirements for gender affirming surgery. We highlight how this suggests a shift to “pubertal bio/logics”: rooting gender/sex definitions in secondary sex characteristics. This contrasts with previous requirements that pointed to “newborn bio/logics”: rooted in genital definitions of gender/sex affirmed by a surgico-medical authority. Both support policy framings of gender/sex as a biophenomenon, though with different impacts for trans and/or nonbinary livability. Our study provides insights into U.S. state definitions of gender/sex, and their multiple and contradictory biological views on gender/sex, with implications especially for transgender and/or nonbinary individuals’ lives.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".