Mobilizing a Slow Theatre Movement through an <i>Atypique</i>. Artist Perspective
Bibliographic record
Abstract
With our performance industry working from turbo-capitalistic frameworks, artists from neurodiverse and other disability communities are all-too-often passed over. This article calls attention to how an emerging slow theatre movement in Canada is changing these conditions. Slowness, as McAskill argues, is an important mode of perception that values human diversity, resensitizing us to the world we move through. McAskill extends this approach with the Quebec-based atypique artistic movement. Dedicated to recognizing the aesthetic value and professional rights of physically unconventional and neurodivergent artists, McAskill describes in what ways atypique artists are leading new legacies of a slow theatre movement, particularly Les Productions des pieds des mains, a Montreal-based dance-theatre company. Discussing her experiences on-set as a production assistant for a recent contemporary film called Eurêka!, created by Menka Nagrani, the Artistic Director of the company, McAskill theorizes on how the company’s artistic approaches serve as a model of slow theatre, particularly through how they work with and value their diverse artists, who include members of the disability community. Ultimately, McAskill emphasizes the need for slowness in current Canadian theatre and, as she argues, its potentiality to set new conditions of art-making and living in North America.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".