Clowning but Not: A Clown’s Approach to Drama for People with Intellectual and Physical Disabilities
Bibliographic record
Abstract
Even without the red nose and costume, clowning has much to offer when used as an approach to teaching creative drama to people with intellectual and physical disabilities. Clown humour and animated play encourage creativity, movement, and self-expression. With a clown-like approach, markers for success change and become more relevant to the level at which participants are working. Using a clown approach allows participants to expand their capacities by releasing the fear of not accomplishing their goals. In fact, slipping up and failing at tasks are good reasons for the group to laugh and relax. In my experience in setting up drama programs at the Manitoba Developmental Centre, I discovered the value of the clown’s flop. By embracing failure, participants were more able to enjoy taking risks. Non-verbal communication skills and object transformation, common to clown play, also created new possibilities for the participants’ creativity and expression. As a result, they were able to move, play, and communicate in unexpected ways and put on a performance for 300 people. The community of therapeutic clowns—including Karen Ridd, Joan Barrington, and organizations like the Dr. Clown Foundation—engages a similar approach to working with the public. They embrace failure and succeed at bringing joy through humour, clown antics, and play.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".