Laws and Policies Regulating Personal Names and Transgender and Gender Diverse Identities in the US and Canada
Bibliographic record
Abstract
Empirical research and anecdotal evidence suggest that having gender-concordant identity documents (IDs) may serve as a protective mechanism to help reduce the harassment, discrimination, and violence experienced by transgender and gender diverse (TGD) adults. The term TGD refers to persons whose gender identity and/or gender expression may not be consistent with the gender identity or expression commonly associated with the sex assigned at birth. It is estimated that nearly 1.5 million people living in the US and Canada identify as TGD. Yet, comparatively few TGD persons in these two nations have gender-concordant IDs, in part due to jurisdiction-dependent complexities in the laws and policies regulating name changes and gender marker changes on IDs such as birth certificates. The purpose of this chapter is to provide an overview of the laws and policies in the US and Canada that regulate the ability of TGD adults to change their name and/or gender marker on IDs. Critical to these laws and policies is the debate of who has the right to control the gender and onomastic declarations on official documentation: the state or the individual.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".