Epistemic violence against trans* people in Iran: unethical medico-legal processes of gender-affirming care
Bibliographic record
Abstract
In this paper, we investigate the ethical and epistemic implications of the medico-legal process of gender-affirming care (GAC) in Iran by focusing on Ayatollah Khomeini’s fatwa on gender-affirming surgery (GAS), as well as legal and medical policies and practices. Hence, we show how despite the legal provisions for gender transition in Iran, trans*Footnote1 individuals face systemic violence and dire living conditions – challenges that are intensified by the entanglement of the medical requirements with the processes of legal gender recognition. Employing Beauchamp and Childress’ (2013) principles of biomedical ethics and Kristie Dotson’s (2011) concept of epistemic violence, we show how the Iranian medico-legal process of gender transition fails to respect the ethical principles of autonomy, nonmaleficence, beneficence, and justice for trans* individuals. Analyzing the legal and medical regulations and processes, we also highlight the ethical breaches that, through testimonial quieting and silencing, contribute to pervasive violence against trans* individuals. Hence, we argue that the process of GAC not only infringes trans* individuals’ rights but also subjects them to multiple forms of violence. To overcome the unethical medico-legal process of gender transition and to remedy epistemic violence against trans* individuals, we propose separating the medical process of GAC from the legal process of gender recognition.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".