Jewish women's sexual behaviour and sexualized abuse during the Nazi era
Bibliographic record
Abstract
Consideration of Jewish women's lives and experiences during the Holocaust became a priority only late in the 20th century. Scholars focused on women's roles as homemakers, wives, breadwinners, supporters and resistors, with little, if any, attention paid to their reproductive or sexual lives. Many considered that the Rassenschande laws shielded Jewish women from the worst horrors of rape and sexual abuse leading to little investigation of this issue. Women were reluctant to speak of such intimate events, and researchers were hesitant to ask about them for fear of causing further hurt. Concern for the sensationalizing of women's experiences also inhibited investigation of this aspect of women's lives. Significant acts of emotional, sexual and physical abuse of women, were, however, perpetrated by the Nazis and others against men and women, Jews and non-Jews, including humiliating nudity, rape and physical abuse. This article focuses on Jewish women's sexual experiences as expressed in diaries, memoirs and testimonies. It explores the variety of interactions that occurred, ranging from loving relationships that emerged despite extremely difficult living conditions, to sexualized humiliation, sexual exchange, rape and sexually related brutality. Recognizing the extent of women's adverse sexual experiences, and their aftermath, acknowledges their lives and honours their experiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".