Bibliographic record
Abstract
The twentieth century witnessed two devastating world wars that led to the exodus of millions of people. Counted among them were hundreds of neuroscientists and biological psychiatrists from Nazi Germany and its surrounding countries who were forced to emigrate in the 1930s and 1940s. Many of them settled in North America, where they profoundly influenced the development of the biomedical sciences. Focusing on the years between 1933 and 1989, Great Minds in Despair examines the long-term effects of this forced migration on scientific and medical cultures in North America and on the researchers themselves. Frank Stahnisch traces the lives and careers of approximately four hundred German-speaking doctors, scientists, and researchers over two generations. Placed in unfamiliar research settings in Canada and the United States, they helped to build the fields of neuroscience, psychiatry, clinical psychology, and the cognitive sciences, even as they rebuilt their own lives amid myriad challenges including cultural adaption and the complications of relicensing. Stahnisch explores how generational factors, gender, international networking, refugee organizations, and national funding agencies shaped their experiences and affected postwar remigration. Great Minds in Despair provides an important revision to the brain gain thesis in migration studies by turning attention to the working conditions and social acculturation of an influential academic refugee group in North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".