An Interview with Dr. Bernie Warren, Clown Doctor and Founde of Fools for Health
Bibliographic record
Abstract
Bernie Warren Ph.D. (A .K .A . Dr . Haven't-AClue) is a full professor at the University of Windsor. His expertise and research spans a vast array of interests that relate to wellness, well-being, and the role of the arts in healthcare and education and are reflected in many articles, books, speaking engagements and participation in international symposiums and conferences. In 2001, he was awarded the Alumni Award for Distinguished contributions to University Teaching. His research and practice brings together his training and interest in Eastern martial arts and healing with his Western training in psychology and performing arts. He has worked with severely disabled children, seniors and people with life threatening medical conditions. His work with therapeutic clowns, "Clown-doctors" as he prefers, has been acclaimed as pioneering work in the field of applied medicine and child life specialties. In this interview, I discuss specifically with Dr. Warren about the role of humour and the work of clown-doctors.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".