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Record W3036450943 · doi:10.3138/ctr.183.006

Clowning but Not: A Clown’s Approach to Drama for People with Intellectual and Physical Disabilities

2020· article· en· W3036450943 on OpenAlexvenueaboutno aff
Sue Proctor

Bibliographic record

VenueCanadian Theatre Review · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDramaCreativityExpression (computer science)PsychologyDrama therapyAestheticsSocial psychologyVisual artsArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.233
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes2
Has abstractyes

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