Bibliographic record
Abstract
As a pioneer in the field of neuropsychology, Dr. Brenda Milner has contributed to many important landmark discoveries in the study of memory and temporal lobes, the lateralization of hemispheric function in language, as well as the role of frontal lobes in problem-solving. She is a fellow of the Royal Society (London) and the Royal Society of Canada, and a Foreign Associate of the National Academy of Sciences (USA). She has been recognized with numerous prestigious awards throughout her career, the latest of which include the Donald O. Hebb Distinguished Contribution Award in 2001, the Neuroscience Award from the United States National Academy of Science in 2004 and the Gairdner Award in 2005. Dr. Milner received her undergraduate degree at the University of Cambridge in 1939 and completed her PhD under the supervision of Dr. Donald Hebb at McGill University in 1952. She joined the Montreal Neurological Institute in 1950 to work with Dr. Wilder Penfield. Dr. Milner is presently the Dorothy J. Killam Professor of Psychology at the Montreal Neurological Institute and the Department of Neurology & Neurosurgery of McGill University. I spent an afternoon with Dr. Milner on May 12th, 2006, where she shared with me her thoughts on her work, her perspective on the past and future of cognitive neuroscience, as well as her advice for students beginning in research.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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".