Lionel Penrose’s Statistical Consultant and Lessons from the Statistical “Sudoku” They Left Us
Bibliographic record
Abstract
Lionel Penrose's Citation1933 study was one of the first to establish that ``the father's age is not a significant factor in the risk of Down's syndrome, while the mother's age is to be regarded as very important.'' We identify the statistical consultant who helped Penrose improve on the prevailing regression methods for handling binary responses. Using a form of ‘statistical sudoku’ we explain how to create complete datasets from the summary data Penrose worked with and shared. And we describe the surprises we got when we applied modern methods to these datasets.
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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 | 0.129 | 0.578 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.026 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".