Detransition needs further understanding, not controversy
Bibliographic record
Abstract
Kinnon MacKinnon and colleagues call for robust, sensitive research to inform comprehensive gender care services for people who detransition In recent years, public discourse has drawn attention to research and clinical practice regarding gender affirming care for transgender, non-binary, and gender diverse (trans) populations. In particular, the phenomenon of gender detransition—discontinuing or reversing gender affirming medical or surgical interventions—has been thrust into the spotlight through a highly publicised legal case in the UK, brought by someone who detransitioned, that challenges the ability of people younger than 16 years to give informed consent to start medical gender affirming treatment.1 At the same time politically driven efforts across the United States are seeking to restrict those under the age of 18 from receiving gender affirming care, citing that limited long term evidence contraindicates gender affirming care for children and adolescents.23 The flurry of media attention has highlighted the complexity underlying the science of gender care and the reality that, for some trans people, gender identity and care needs may change over time. Yet media stories about detransition often disproportionately feature those who want to limit access to treatments for gender dysphoria. Understanding the full range of experiences and perspectives of people who detransition—who may be referred to as detransitioners or detrans people—is crucial to advancing the field of gender care (box 1). Box 1 ### Glossary of key concepts relevant to research and practiceRETURN TO TEXT
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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 | 0.028 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".