The Just Prison? Women’s Prison Reform and the Figure of the “Offender-as-Victim” in Germany
Bibliographic record
Abstract
During the 1990s, the Berlin women’s prison was reformed to do justice to female inmates. This redesigning of space and programs was intended to meet women-specific conditions and needs. The present paper engages with this prison reform as transformation in the name of gender justice. Based on interviews with prison reformers, criminologists, and policymakers, as well as on the analysis of historical documents, we illuminate how a specific figure of the “criminalized woman” helps to translate the abstract notion of social justice into situated practice. From the 1970s onward, a new knowledge of women’s crime would emerge: it constituted female offenders as victims of patriarchal oppression and victimization, allowing the prison system to be criticized as androcentric and discriminatory against women. We argue that subsequent reform pursued gender justice in the form of difference-based, gender-responsive programs and spaces targeting individual inmates’ character and mindset. Thereby, the reformers’ initial critique of social justice would be unintentionally depoliticized and so gender, economic, and political inequalities remained unaddressed. Our purpose is hence twofold: first, to review the recent history of women’s incarceration in Germany, and second, to add a social justice focus to the international criminological debate on gender, prison, and reform.
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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 | 0.002 |
| 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 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".