“Reason for Dismissal? — Jewish Faith”: Analysis of Narratives in the SPSL Immigration Applications by German-Speaking Neurologists
Bibliographic record
Abstract
Two months after Adolf Hitler (1889–1945) had been proclaimed the Reich chancellor, the first anti-Jewish law was passed in Nazi Germany, based on which “non-Aryan” academics and researchers were dismissed from their state-supported positions. These scholars were desperate to flee Germany, due to the appalling treatment they had been subjected to regardless of their academic status and scientific achievements. The growing socio-political tensions in Germany attracted considerable attention from British scientists, who — led by Sir William Beveridge (1879–1963) — established the Academic Assistance Council (later known as the Society for Protection of Science and Learning; SPSL). Between 1933 and 1945, the SPSL assisted several thousand scholars in need by providing stipends and placements at universities or research institutions in the United Kingdom and elsewhere. Among the fortunate emigres were world-renowned professors as well as upcoming young scientists. Regardless of their level of expertise, these young academics and physicians were equally distressed by the way they were treated and desperate to flee Germany. The SPSL immigration questionnaires and other supporting materials provide an insight today into the events, which the applicants experienced at the time. They furthermore present their hope to rebuild their lives and careers in their new host country in considerable detail. This article analyses the work and family life of German-speaking neuroscientists as well as the political context and SPSL responses to Nazi and British policies. It focuses on applicants’ social and scientific context at the time, by also emphasizing how the drastically worsening situation in the Third Reich affected refugees’ morale and increased their efforts in escaping the country. The case of emigre neuroscientists is particularly insightful, as this group encompassed an interdisciplinary and heterogeneous group of psychiatrists, neurologists, psychologists, and experimental biologists, which allows for useful cross-comparisons.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".