(De)trans visibility: moral panic in mainstream media reports on de/retransition
Bibliographic record
Abstract
The current increase in the visibility of trans people in the media has been accompanied by a backlash in the form of an increased deployment of narratives of ‘sex change regret’ or ‘de/retransition.’ Through analysing mainstream media articles from 2015–2018, this paper identifies and discusses three main themes detected in discussions of de/retransition. First, the articles claim that the social and political climate has become too accepting of trans identities and, thus, any discussion of de/retransition is silenced because of ‘political correctness.’ Second, while the articles collected tend to begin with a general discussion of the phenomenon of de/retransitioning, they slide into addressing (White, cisgender) children and the need to protect them from misdiagnosis. Third, the fear about misdiagnosis of (White, cisgender) children is intensified by the focus on a recently hypothesised category of gender dysphoria – rapid-onset gender dysphoria – that suggests some children’s and adolescents’ dysphoric feelings are a result of ‘social contagion.’ Mainstream media discussions of de/retransition focus on the aforementioned themes in an attempt to question contemporary approaches to regulating access to gender-affirming medical care for trans individuals.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".