Bruno Schulz's 1936 book “Methodology of medical genetic research particularly with regard to psychiatry”
Bibliographic record
Abstract
In 1936, Bruno Schulz published the first detailed, book-length review of the methodology of psychiatric genetic research, based on his experiences at the German Research Institute of Psychiatry. Emphasis is placed on proper selection of relatives and the ascertainment corrections required for Mendelian transmission models. Twin studies are considered as is the impact of reduced fertility on patterns of risk. For the field work, Schulz emphasizes the importance of trust-building, confidentiality, collateral informants, and the use of medical and other administrative records, all ideally stored in personal files. Several methods of age-correction are reviewed. Schulz provides detailed algebraic treatments of these and other problems, including tests for etiologic homogeneity, with worked examples. He emphasizes two fundamental concerns in psychiatric genetics research: (i) its inter-dependency with the optimal diagnostic boundaries, which are rarely known and (ii) the genetic homogeneity of clinical samples. Given these problems, he is pessimistic about finding Mendelian transmission patterns. He assesses the predominant 19th-century method of psychiatric genetic investigation-"hereditary burden"-to be crude and biased by family size. Although written at a time of consolidation of Nazi power in Germany, this book nowhere endorses their racial/eugenic policies and can be seen as subtly questioning them.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".