Nurses, medical records and the killing of sick persons before, during and after the Nazi regime in Germany
Bibliographic record
Abstract
During the Nazi regime (1933-1945), more than 300,000 psychiatric patients were killed. The well-calculated killing of chronic mentally 'ill' patients was part of a huge biopolitical program of well-established scientific, eugenic standards of the time. Among the medical personnel implicated in these assassinations were nurses, who carried out this program through their everyday practice. However, newer research raises suspicions that psychiatric patients were being assassinated before and after the Nazi regime, which, I hypothesize, implies that the motives for these killings must be investigated within psychiatric practice itself. An investigation of the impact of the interplay between the notes left by nurses and those by psychiatrists illustrates the active role of the psychiatric medical record in the killing of these patients. Using theoretical insights from Michel Foucault and philosopher Giorgio Agamben and analyzing one part of a particularly rich patient file found in the Langenhorn Psychiatric Asylum in the city of Hamburg, I demonstrate the role of the record in both constructing and deconstructing patient subjectivities. De-subjectifying patients condemned them to specific zones in the asylum within which they were reduced to their 'bare life'--a precondition for their physical assassination.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".