Sisters in Arms: Militant Feminisms in the Federal Republic of Germany since 1968
Bibliographic record
Abstract
Katharina Karcher’s study shows that despite the historical and political shifts since the 1960s, the fervent debates that took place in that decade on militancy and the use of violence in pursuit of political goals still hold relevance today. Yet, the impact and afterlife of feminist militancy remains under-explored in most histories of West German feminism. Karcher’s study takes an important step toward filling this void. While framing feminist militancy within the broader context of political developments and feminist struggles and debates from the famous 1968 tomato attack by SDS women on SDS male leadership to Pussy Riot’s protest action in the Moscow Cathedral of Christ the Saviour in 2012, the study’s primary focus is on the 1970s and 1980s. In tracing and analyzing the history of feminist militancy, Karcher seeks to disrupt the narrative of an antagonism between ‘good feminism’ and ‘bad militancy’, arguing that this assessment rests on a limited understanding of both. She conceives of feminist militancy as one important—albeit understudied—aspect of feminist activism, and aims to analyze it in its interaction with other methods of feminist protest and intervention. Her study contributes to undermining the assumption that feminism is inherently and necessarily peaceful. As the militant feminist group Red Zora, a core subject of the book, stated, it ‘“found it tremendously liberating to break with the feminine peaceableness that was imposed on us and to take a conscious decisions for violent means in our politics”‘ (p. 8).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".