Irma Weinberg's 1928 paper “on the problem of the determination of heredity prognosis: The risk in the cousins of schizophrenics”
Bibliographic record
Abstract
Irma Weinberg, a German-Jewish Neuropsychiatrist/Physician, authored the fourth report from the German Research Institute for Psychiatry in Munich examining the risk for dementia praecox (DP) in particular relatives of DP probands, here first-cousins. She examined 977 cousins of 54 DP probands and found a best-estimate risk of 1.4%. She conducted within-study analyses, showing a much higher risk for DP in the siblings than cousins of DP probands. She studied DP-related personalities showing a familial link between these conditions and risk for DP. She demonstrated that the risk for DP in cousins was impacted substantially by the distribution, in ancestors, of psychosis and personality abnormalities. After completing work on this article, Weinberg worked in private practice in Frankfurt, emigrating to the Netherlands in 1934, where she worked at a Jewish psychiatric hospital. In 1943, German occupiers evacuated the hospital, transporting the patients and staff, either directly to Auschwitz or, like Weinberg, to the Westerbork transit camp. On September 4, 1944, Dr. Weinberg was transported to Theresienstadt and soon thereafter to Auschwitz, where she was murdered at the age of 53. Her history raises painful questions about the relationship between genetic studies of psychiatric illness in prewar Germany and the Holocaust.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.001 | 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".