Bryan Kolb: Pioneer in neuropsychology and role model for teaching, mentoring, and involving undergraduates in neuroscience research
Bibliographic record
Abstract
Field Neurosciences Institute, 4677 Towne Centre, Saginaw, MI 48604. Bryan Kolb is a native of Calgary, Canada and is currently a Professor in the Neuroscience Department at the University of Lethbridge, where he has been since 1976. He received his PhD from Pennsylvania State University in 1973 and did postdoctoral work at the U of Western Ontario and the Montreal Neurological Institute. His recent work has focused on the development of the prefrontal cortex and how neurons of the cerebral cortex change in response to various developmental factors including hormones, experience, stress, drugs, neurotrophins, and injury, and how these changes are related to behavior. Bryan Kolb has published five books, including two textbooks with Ian Whishaw (Fundamentals of Human Neuropsychology, Sixth Edition; Introduction to Brain and Behavior, Third Edition), and over 300 articles and chapters. Kolb is a Fellow of the Royal Society of Canada and a Killam Fellow of the Canada Council. He is currently a member of the Canadian Institute for Advanced Research program in the Experience-Based Brain Development program. Bryan Kolb has won numerous teaching and research awards during his illustrious career, and his experience with mentoring undergraduate students provides an excellent model to emulate, for both young and seasoned faculty members who strive to provide their students with the best possible learning opportunities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".