Radical Feminisms in the U.S. and France: #BalanceTonPorc, #MeToo, and the Migration of “Dangerous” Gender Ideologies
Bibliographic record
Abstract
This essay traces the movements of radical feminist work between the U.S. and France, arguing that the long dialogue between American gender studies and French Feminisms has reached a new and extremely important point with the emergence of the #BalanceTonPorc, #MeToo, and the rise of the far Right in since 2016. More specifically, I analyze the transatlantic exchange of radical feminist theoretical work with two specific contexts in mind. The first is the flow of ideas between French and American feminists during the 1970s and 1980s and the profound impact of French feminisms on the U.S. academy and on American feminist political thought. “French Feminism” was instrumental in reorienting academic disciplines to the study of the social construction of gender and language and in the institutionalization of Women’s Studies and Gender Studies programs in the U.S. I then explore how more recently, politicians and popular figures in France have accused French feminists of importing “dangerous” gender ideologies from the U.S. and lamented the “Americanization” of the French intellectual tradition. As panic spreads about what Macron’s education minister deemed the “intellectual matrix from American universities,” we have witnessed the rise of #MeToo in the U.S. and #BalanceTonPorc in France, both of which aim to name and publicize the quotidian experience of sexual violence.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".