Bibliographic record
Abstract
With the passing this spring of Margaret Norman, MD, FRCP(C), the American Association of Neuropathologists and indeed the worldwide neuropathology community have lost a wonderful scholar and practitioner and a remarkable human being. Pediatric neuropathologist extraordinaire, author, researcher, mentor, ethicist, confidante, friend, and humanitarian, Margaret's example of personal and professional behavior, achievement, and dedication set a standard to which all of us, and those who follow us, should aspire. Perhaps her upbringing predestined her for a lifetime of selfless service. She was born in Japan as the child of United Church missionaries and grew up in Vancouver. She obtained her medical degree from the University of Toronto and worked as a government doctor in Frobisher Bay in the Northwest Territories among the least advantaged of the Canadian population before returning to Toronto where she specialized in pathology and then pediatric pathology at the Hospital for Sick Children. After a further fellowship in pediatric pathology at Columbia in New York, she specialized in pediatric neuropathology at the Massachusetts General Hospital before returning to Toronto as a staff neuropathologist at the Hospital for Sick Children in 1970. She moved to the Children's Hospital of Eastern Ontario at the University of Ottawa as associate professor and chief pathologist where she remained for 6 years before moving to the University of British Columbia where she worked at the British Columbia Children's Hospital, achieving the rank of professor. She remained there until her retirement as professor emeritus in 1996.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.325 | 0.159 |
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".