Practising Diversity at the Stratford Festival of Canada: Shakespeare, Performance, and Ethics in the Twenty-First Century
Bibliographic record
Abstract
What does it mean to ‘practise’ diversity in Shakespeare production in the twenty-first century, specifically in an Anglo-American context? How is ‘practising’ diversity, from devising and directing to work in the rehearsal hall and on audience engagement, materially different from the now-familiar (but still important) goal of ‘representing’ diverse bodies on stage? In the last twenty years, debates about what the diversification of Shakespeare performance – along racial lines, gender lines, the lines of age and ability – means or could mean, and the simultaneous interrogation of what ‘Shakespeare’ signifies, for whom, and to whose benefit, have become increasingly urgent issues for scholars and artists. If theatre companies across the Anglosphere increasingly share the assumption that diversity and inclusion, in both the casting and creation of Shakespeare in performance, is necessary and good for ethical and artistic reasons, what tools, resources and attitudinal shifts are required in order for those companies to move beyond representations of difference on stage, and toward engaging deeply with equity and diversity as conditions of theatrical production and reception?
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.048 | 0.038 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".