Fascism and the Trans Villain: Historically Recurring Transphobia in Far-Right Politics
Bibliographic record
Abstract
This article builds a base of historical and theoretical context to understand the resurgence of transphobic propaganda and violence led by the American far-right through an examination of the connections between trans politics and global political economies of capitalism. Through a synthesis of established theories of fascism, a historical analysis of fascism, and a case study of propagandistic transphobia in two American films from the height of the Cold War, I argue that the proliferation of contemporary anti-trans sentiment reflects the state of crisis that the American empire is experiencing as domestic and international resistance threatens its global hegemony. Further I argue that a historical and theoretical examination of fascism and trans issues show the capacity for fascistic anti-trans violence not as a departure from the norm of liberal democratic nation-state systems that developed through the processes of capital, but rather as a constitutive part of that norm. Trans historians must mobilize historical knowledge and practice to disseminate public facing works that furnish a wide base of readers with the tools to understand and contextualize contemporary trans panic as it metastasizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".