Augmented Reality ASL for <i>11:11</i> at Theatre Passe Muraille
Bibliographic record
Abstract
In November 2020, Theatre Passe Muraille produced a workshop as a part of their Accessibility Labs (funded through the Toronto Arts Council’s Open Door Project). This workshop connected the development of Samson Bonkeabantu Brown’s play 11:11, directed by Tsholo Visions Khalema and choreographed by Mafa Makhubalo, with a collaboration between technology collectives Toasterlab and Cohort to experiment with the use of augmented reality to support the American Sign Language interpretation and live captioning in consultation with Courage Bacchus, Marcia Adolphe, and Carmelle Cachero, Jenelle Rouse, and Gaitrie Persaud. The collaborators spent a week building an interpretation distribution system that used affordable technology to create a proof-of-concept experiment to see what it would look like to move both text and sign interpretation from just offstage into the visual field of the performance. Using Cohort’s media distribution app developed for the synchronous delivery of media to mobile devices, this project explored live and pre-recorded versions of performance interpretation for a Deaf audience. These were displayed on the audience’s phone screens and in simple augmented reality headsets. The goal was to explore expanding the opportunities for an audience to access interpretation, making the experience more customizable through different forms of engagement, and seeking to provide more universal access across all performances by making a recorded alternative available. The workshop also explored the dramaturgical and scenographic implications of adding this information into the direct visual field of an audience member. This article, in the form of a compiled oral history of the workshop, documents the process, the findings, and the follow-up questions that the team identified over our short time together to provide a baseline for further exploration into the use of mixed-reality technologies in support of accessible performance spaces. It considers situated identity and Deaf culture in relationship to translating interpreted performance to a technological solution as it both outlines the practical steps that allowed this to happen and explores artist, interpreter, and technologist perspectives on what was learned.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.155 | 0.032 |
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".