How to lose a culture war: Social media, trans disinformation and the authoritarian right
Bibliographic record
Abstract
Digital technologies are foundational to the authoritarian Far Right plan to “eradicate transgenderism” from public life. In this commentary, we sketch out how the Far Rights has weaponized digital anti-trans disinformation to bolster authoritarianism in the United States. Far Right actors have framed social media platforms as causing transness at the same time that they wield the same tools themselves to create and distribute disinformation about trans people and wage a culture war against “gender ideology.” We note how anxieties over both reproduction in service to the nation state and the potential fertility of gender nonconforming children and adolescents have been central to the production of this trans moral panic. Second, we ask, how might trans people create digital information ecologies beyond mere opposition to anti-trans disinformation? Rather than perpetually taking a reactive, or reactionary, stance to the constant influx of trans disinformation, we consider the political potential of alternative trans information ecologies.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".