Critical Conversations: Emerging BIPOC Critics Reimagine Theatre Criticism through the Digital
Bibliographic record
Abstract
Abstract: Theatre criticism in the Global North currently finds itself at a pivotal point, as the internet eclipses legacy media as the central space for critical discourse and as ongoing antiracist and anticolonial activism highlights the underrepresentation of BIPOC artists across the theatre industry. Within this context, stakeholders in the theatre community, recognizing the importance of a vibrant critical discourse, are faced with finding more sustainable and equitable models. This article considers the future of theatre reviewing through a case study of Taking on the World (TotW), a mentorship program for emerging BIPOC critics run in conjunction with Toronto's Soulpepper Theatre Company and Intermission magazine. TotW participants' innovative critical practice demonstrates that de-hierarchizing theatre criticism necessitates reimagining the form, and specifically centering conversation as a key quality of process and product. Exploiting the capabilities of the digital, TotW participants embed conversation within their work and model a mode of criticism that challenges traditional notions of expertise and is ultimately more inclusive. Applying recent scholarship on theatre criticism to examples of work produced in the program and participant interviews, this article advances new ways of practicing and teaching theatre criticism at this crucial moment.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.027 | 0.042 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".