Punishing gender-affirming care: social media and U.S. anti-trans politics
Bibliographic record
Abstract
Abstract Political communication relies heavily on digital platforms that reward hostile content with greater audience engagement. Following Executive Orders 14168 and 14187 in January 2025, the Trump administration orchestrated a rapid, multi-agency dismantling of institutional support for gender-affirming care. This article argues that these orders functioned simultaneously as administrative mandates and as social media content designed to discipline institutional infrastructure through an antagonistic “gender ideology” framing. The study traces how the directives became entangled with platform dynamics to consolidate affective publics around punitive state action. Following the orders, Trump mentions escalated from 0.9% to 80.2% of high-engagement transgender discourse, while the “gender ideology” frame became predominant, displacing neutral language. Rather than targeting individuals directly, the orders transformed bureaucratic retrenchment into an iterative social media spectacle. This study demonstrates how state authority leverages platform dynamics to mobilize publics around institutional punishment, framing digital populism as an enduring element of American governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".