Re-Contextualizing Canadian Clown: In Conversation with Monique Mojica, Jani Lauzon, Rose Stella, and Gloria Miguel
Bibliographic record
Abstract
In this article, theatre artist/researcher Sonia Norris delves into the complicated question, “what is Canadian clowning?” Canadian clowns provide a diverse array of possibilities in answer to this question depending on their location, training, and linguistic and cultural background. However, the international clowning community often associates the term ‘Canadian clowning’ uniquely with the style of clown training and performance developed by Richard Pochinko in the seventies and eighties. Furthermore, their perception is that this style of clowning is a form of Indigenous or ‘Amerindian’ clowning. Norris scrutinizes this perception with Indigenous theatre artists Monique Mojica, Jani Lauzon, Rose Stella, and Gloria Miguel. Lauzon and Mojica worked with Richard Pochinko and his longtime collaborator Ian A. Wallace, Stella worked with Pochinko-trained teachers as part of her clown training, and Miguel’s work with Spiderwoman Theater incorporates clown and Trickster. This discussion presents a re-contextualization of the term ‘Canadian clown’ and investigates the differences and connections between Pochinko’s approach to clowning and Indigenous clowning and Trickster traditions in Canada.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".