Bibliographic record
Abstract
This report reviews the research and mentoring career of Richard B. Stein (1940-). In 1962, he completed a B.S. degree in physics at the Massachusetts Institute of Technology, USA, and thereafter an M.A. (1964), Ph.D. (1966), and postdoctoral training (1966-68) at the University of Oxford, UK. He subsequently assumed a faculty position at the University of Alberta (Canada), where he is currently an active researcher and mentor. To this point in 2004, Stein has trained and collaborated closely with over 160 scientists, largely neuroscientists and biomedical engineers, from 27 countries. He and his former trainees and collaborators have made important contributions on topics that span the cellular-to-behavioral spectrum of movement and rehabilitation-prosthetics neuroscience. His mentors, trainees, and collaborators include scientists whose countries of origin are: Australia, 2; Austria, 1; Belgium, 1; Bulgaria, 1; Canada, 64; China, 6; Denmark, 1; Germany, 1; Great Britain, 16; Hong Kong, 4; India, 5; Iraq, 2; Italy, 2; Japan, 10; Kenya, 1; New Zealand, 4; Pakistan, 1; Palestine, 1; Poland, 1; Romania, 1; South Africa, 1; Sri Lanka, 1; The Netherlands, 1; Turkey, 1; Uruguay, 1; USA, 21; and Yugoslavia, 6. In all instances, Stein's research collaborations and mentoring have advanced the careers of his trainees and junior collaborators, a well-deserved and important compliment to a stellar movement neuroscientist.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".