Zielgruppe Arzteschaft: Arzte als inoffizielle Mitarbeiter des Ministeriums fur Staatssicherheit
Bibliographic record
Abstract
East Germans enjoyed free and universal access to medical care, free prescription drugs, and a sophisticated system of Polikliniken: health clinics that housed a range of medical services short of major surgery that are today considered a model of health organizations. Yet it is likely that every major medical centre in East Germany had at least one informant for the Ministry for State Security (Stasi), that approximately 3–5% of East German physicians were Stasi informants (a rate considerably higher than the roughly 1% in the general population), and that a significant percentage of those informants broke the Hippocratic oath by informing on patients. What are historians to make of health care in the GDR, or even of the dictatorship itself? Can a regime be truly ‘caring’ and ‘coercive’ at the same time, as claimed of late by so many historians of the GDR? Francesca Weil's outstanding work, an empirically based study of physicians who were Stasi informants, reveals the close ties between the repression apparatus and the health system. Based primarily on a review of 493 physician-informant files and twenty-one interviews from these informants, Weil examines a number of key questions that have become standard in studies of informants: the content and consequences of informant reports, motives to become an informant, Stasi recruitment tactics, and reasons behind the termination of the relationship.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".