Neuroscience in Nazi Europe Part III: Victims of the Third Reich
Bibliographic record
Abstract
In Part I, neuroscience collaborators with the Nazis were discussed, and in Part II, neuroscience resistors were discussed. In Part III, we discuss the tragedy regarding european neuroscientists who became victims of the Nazi onslaught on “non-Aryan” doctors. Some of these unfortunate neuroscientists survived Nazi concentration camps, but most were murdered. We discuss the circumstances and environment which stripped these neuroscientists of their profession, then of their personal rights and freedom, and then of their lives. We include a background analysis of anti-Semitism and Nazism in their various countries, then discuss in depth seven exemplary neuroscientist Holocaust victims; including Germans Ludwig Pick, Arthur Simons, and Raphael Weichbrodt, Austrians Alexander Spitzer and Viktor Frankl, and Poles Lucja Frey and Wladyslaw Sterling. by recognizing and remembering these victims of neuroscience, we pay homage and do not allow humanity to forget, lest this dark period in history ever repeat itself.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".