A Comparative Analysis of the Treatment of Transgender Prisoners: What the United States Can Learn from Canada and the United Kingdom
Bibliographic record
Abstract
The treatment of transgender people while incarcerated is an issue that was catapulted into the national spotlight in 2019 when the Ninth Circuit Court of Appeals ruled in favor of providing a transgender inmate gender confirmation surgery. This decision created a circuit split within the United States regarding the medically necessary treatment a state is legally required to provide an inmate with gender dysphoria. This Comment examines the legal and policy approaches of the United Kingdom and Canada to inform the current circuit split and provide suggestions for improvements that could be made in the United States. Through exploration and comparison, this Comment proposes changes in policy and practice for housing transgender inmates, providing consultation with medical professionals, and treating transgender inmates in daily life. More specifically, this Comment proposes that the United States should employ a more gender-affirming approach when providing healthcare to transgender inmates by adopting policies and regulations in accordance with the medical community’s most recent recommendations. This includes setting up data collection systems able to obtain accurate population statistics for transgender inmates to make sure their needs are met. Additionally, uniform policies should be created to align gender-affirming language and practice, and housing reforms are necessary to ensure transgender inmates avoid solitary confinement placement because they lack alternative housing.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".