Talk-backs, Relatability and the Pedagogical Challenges of Performance Analysis
Bibliographic record
Abstract
Years of anxiety, awkward silences, averted eyes and damaged egos compel us to raise the question: Why is the talk-back so consistently dreaded and dissatisfying? Why, in the words of a colleague, does it “never seem to work” and finally, how can we account for the rarity of a felicitous talk-back? The anxieties surrounding post-performance discussions are perhaps symptomatic of a lacuna in the discipline of performance analysis that has, arguably, inhibited its widespread integration into post-secondary theatre studies curricula, which has long privileged dramatic literature and methods of analysis derived from literary criticism. How have post-secondary theatre curricula accommodated the disciplinary tide that has shifted away from dramatic literary models to performance studies, and concurrently, from an analysis of literary texts to “performance texts”? The dearth of courses devoted to developing the analytic skills necessary to effectively “read” a performance event is, we suspect, a by-product of philosophical problems in the field of performance analysis and debates concerning the merits of a universal discourse and its application. As Patrice Pavis writes, performance analysis is a “fallow field that has as yet failed to develop a satisfactory method of universal application. In order to assess the current situation and future possibilities … we have to run the risk of treading on the odd mine” (2).
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 | 0.035 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.015 | 0.093 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".