What exactly is an apple pie? Performative arts and pedagogy: Towards the development of an international glossary
Bibliographic record
Abstract
Please note that this is a slightly edited version of the group discussion. Scenario wishes to acknowledge the vital contribution of Josephine Rutz by expressly thanking her for the transcription of the discussion. MS: Welcome everyone to this afternoon’s group discussion as part of the 4th SCENARIO FORUM Symposium. As you have read in the Symposium programme the German professional association Bundesarbeitsgemeinschaft (BAG) Spiel & Theater e.V. aims to develop an international glossary of key terms in the area of applied drama and theatre and has invited professionals from outside Germany to become involved in this project. Thank you for coming along to this session which is the first brainstorming session on the topic of an international glossary in the area of Performative Arts and Pedagogy. The participants in today’s group discussion are based at institutions in English speaking countries. I wish to thank especially our guests from abroad for their contributions to the Symposium: Barbara Schmenk from Canada, University of Waterloo; Katja Frimberger from Britain, Brunel University, London and Mike Fleming, University of Durham; and, of course, also a big thanks to my university colleagues Róisín O’Gorman and Bernadette Cronin, based in the Department of Drama and Theatre Studies ...
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".