Bibliographic record
Abstract
<JATS1:p>What are the key elements that go into creating a work of art for the stage? Which are the most productive conditions and methods of rehearsal? In this collection of interviews, 19 international artists share their experience and offer practical advice on the creation of performance work. Their answers provide a goldmine of tried and tested approaches as they discuss the common problems and difficulties of creative work, their turning-point experiences, and ways in which they have challenged performers and themselves to go beyond conditioned reflexes to create work which is original and authentic.</JATS1:p> <JATS1:p>Among the contemporary directors, choreographers, performers, and actors to share their insights are: Annabel Arden (Complicite, UK), Richard Lowdon (Forced Entertainment, UK), Richard Foreman (Ontological-Hysteric Theatre, USA), Sean Patten, Berit Stumpf (Gob Squad, UK), Maxine Doyle (Punchdrunk, UK), Soheil Parsa (Modern Times Stage Company, Canada), Eugenio Barba (Odin Teatret, Denmark), Jan Fabre (Belgium), Heiner Goebbels (Germany), Jerome Bel (France), Helgard Haug (Rimini Protokoll, Germany)</JATS1:p>
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 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".