Introduction to the Special Issue
Bibliographic record
Abstract
Don Stuss was one of a kind. As a scientist, he blazed trails in neuropsychology and neuroscience. As a person, he was a great mentor, friend, and bon vivant.The influence of Don's clinical and theoretical work on pFC and consciousness is evident through the breadth of the contributions to this special issue, ranging across age groups and methodologies, from cognitive-behavioral to multimodal neuroimaging (structural MRI, fMRI, and scalp and intracranial EEG), in neurotypical and clinical populations (attention-deficit/hyperactivity disorder, stroke, tumor, neurodegeneration, and schizophrenia).A common theme that runs through them all is a focus on a clinically informed psychological architecture of the highest forms of human cognition: attending, thinking, decision-making, and cognitive control. Don influenced the field by astute clinical observations of disruptions in these phenomena in patients, and he translated these into testable hypotheses.Don's vision as a scientific leader was recognized when he was selected to lead the Rotman Research Institute at Baycrest in 1990. The institute grew from a handful of people to a world-leading center for cognitive neuroscience, including neuroimaging and neuroinformatics, which were not part of his primary research methods. Don's influence reverberates through the hundreds of trainees who have passed through the Rotman Research Institute, many of whom are now leading investigators in their own right. Don's vision expanded further with his leadership of the Ontario Brain Institute, which became a model worldwide for integrated discovery and clinical informatics.No mention of Don is complete without acknowledging his humility, generosity, good humor, and friendship. The remembrance by Alexander, Picton, and Shallice (2020) is a fitting introduction to Don's history as a scientist and as a person (see also Craik & Levine, 2020; Levine & Craik, 2020).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.356 | 0.243 |
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".