Defying the Monolingual Stage / Bousculer la scène unilingue
Bibliographic record
Abstract
In early April 2017, Toronto’s Modern Times Theatre invited a diverse group of artists, scholars, and critics to join a discussion about diversity in Canadian theatre practices. One of the panels moderated by the Artistic Director of Cahoots Theatre, Marjorie Chan, focused on languages and accents on stage. Each of the discussants proposed their own set of questions: How can minority languages be represented on stage? Should they be translated? What is the role of subtitles and what kind of sub/surtitles should be used? Who is allowed to use which language? For instance, can hearing actors use ASL on stage or should they let deaf actors perform roles that require ASL? Should immigrant actors who learned English as adults be expected to speak English without a marked accent? Why do Canadian audiences and critics find it difficult to accept “non-native sounding” actors performing characters that are expected to have an “unmarked” accent? Why are they expected to have an “unmarked accent”? While the discussants did not see eye to eye on many of these issues, it was clear that they all shared the view that professional Canadian theatre companies and Canadian theatre schools are currently doing a rather poor job at fostering linguistic and phonetic diversity on stage. It also became clear that the question of using multiple languages on stage is profoundly intertwined with the question of accents, dialects, the issues of accent/language perception, as well as race and race perception, the problem of power distribution, and, last but not least, the aesthetic choices of every single production.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".