Scientific Responsibility and Political Context: The Case of Genetics under the Swastika
Bibliographic record
Abstract
INTRODUCTION Study of biology flourished under the swastika. Although we tend to dismiss Nazi science as pseudoscience and equate research in biology with racial hygiene, the history of biology during the Third Reich was in fact quite complex. Work supported by Heinrich Himmler's Das Ahnenerbe (“ancestral heritage”), the research and teaching arm of the Schute-Staffel (SS), was indeed racist nonsense (Deichmann 1996, pp. 251–76). But most of the science supported by the Deutsche Forschungsgemeinschaft (DFG), the major government funding agency, would have been considered mainstream science in the 1930s and 1940s. Its content and standards differed little from those of the science being pursued elsewhere in the western world. Of course, research under the Third Reich was funded in the expectation that it would ultimately advance the aims of the regime. That fact prompts us to ask how we should think about the activities of scientists who did not engage in overtly criminal acts, but rather practiced “normal research.” We do not hesitate to condemn researchers who actively promoted and implemented the racial policies of the National Socialist state. We know what to think about those who produced anti-Semitic propaganda or reports on racial ancestry in connection with enforcement of the Nuremberg laws, helped formulate euthanasia policy, informed on colleagues who employed half-Jewish or politically suspect assistants, or conducted obscene experiments on human subjects. But more difficult, and more interesting, questions are raised by the behavior of scientists whose work was no more racist in either its intention or its assumptions than that of their non-German peers, but who in some way sought to profit from the National Socialist regime.
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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 | 0.001 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".