Delegated performance: Interdisciplinary tensions, provocations and questions
Bibliographic record
Abstract
Abstract Art historian and critic Claire Bishop and theatre/performance scholar Jen Harvie have each written about a relatively new form of socially engaged participatory performance art that Bishop calls ‘delegated performance’. Delegated performance differs from earlier forms of performance art in that the performance is delegated to other people (hired or volunteer), including (at times) spectators, who enact the piece, rather than the piece being carried out by the artist herself. This set of poetically informed microlectures examines delegated performance in the attempt to glean interdisciplinary understandings about how the delegation of performance happens in drama education and applied theatre. What might those who work and research in these fields consider as issues surrounding the delegating performance? Of what value is this notion in assisting in thinking about practice? Examples of delegated performance are drawn from Bishop and Harvie and additional sources and considered for the ethical questions and provocations they raise. In light of this investigation, drama educators and applied theatre facilitators are recommended to consider how much and in what ways they are delegating performance with students/participants, and to what effect.
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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 | 0.058 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.103 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".