Tatia Lee: Neuropsychology and human neuroscience research insights inform the theoretical and translational framework for fostering brain and psychological health
Bibliographic record
Abstract
Tatia Lee, PhD, is Chair Professor of Psychological Science and Clinical Psychology at The University of Hong Kong, a clinical psychologist, and a board-certified clinical neuropsychologist. Her research focuses on the neuroplastic basis of neurocognitive and affective processes underpinning normal and pathological neurocognitive and psychological functions. She is an elected Fellow of esteemed international societies, including the World Academy of Sciences, Academy of Social Sciences (UK), American College of Professional Neuropsychology, Association for Psychological Science, and American Psychological Association. She is the Founding Chair of the Clinical Neuropsychological Society under the Chinese Cognitive Science Society of China. She has received numerous awards and recognition, including The University of Alberta Alumni Horizon Award, the Fulbright Hong Kong Scholar, the State Scientific and Technological Progress Award, and the Humanities and Social Sciences Panel Prestigious Fellowship Award. Acknowledging her substantial contributions and academic achievements in advancing neuropsychological sciences, The University of Hong Kong bestowed her the “May Endowed Professorship in Neuropsychology.” Professor Lee is happy to provide our readers with reflections on her life and career.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".