Émigré neurophysiologists' situated knowledge economies and their roles in forming international cultures of scientific excellence
Bibliographic record
Abstract
This article investigates the scientific performance and impact of Jewish and politically oppositional émigré German-speaking neurophysiologists from Nazi-occupied Europe since the 1930s. The massive loss of nearly 30% of all academic psychiatrists and neurologists in Germany between 1933 and 1945 also shattered the basis of German-speaking neuroscientific research. A focus will be laid here on the contingency of situated knowledge economies in Central Europe, the UK and North America, as well as their roles in the formation of international cultures of scientific excellence in the forced migration process. While examining excellent émigré laboratory research, the intriguing biographies of three Nobel Prize-winning neurophysiologists––Otto Loewi (1873–1961; from Germany/Austria to the USA), Bernard Katz (1911–2003; from Germany to the UK) and Eric Kandel (b. 1929; from Austria to the USA)—can tell us considerably more about the appraisal of medico-scientific knowledge through an epistemic lens representing world history along explicit regional knowledge economies. This article examines some of the more intricate scientific practices and professional patterns of determining academic excellence related to situated knowledge communities in the contemporary brain sciences.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.011 |
| 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".