The potentials of spaces: the theory and practice of scenography & performance
Bibliographic record
Abstract
'Introduction: The Potentials of Spaces' - Page 11 - Alison Oddey & Christine White 1: 'Directors and Designers: Is there a different direction?' - Page 25 - Pamela Howard 2: 'Different Directions: The potentials of autobiographical space' - Page 33 - Allison Oddey 3: 'Collaborative Explorations: Reformulating the boundaries of scenographic practice' - Page 51 - Roma Patel 4: 'Flatness and Depth: Reflections' - Page 61 - Nick Wood 5:' Digital Dreams: Sleep Deprivation Chamber' - Page 69 - Lesley Ferris 6: 'Re-Designing the Human: motion capture and performance potentials' - Page 85 - Katie Whitlock 7: 'Smart Laboratories: New media' - Page 93 - Christine White 8: 'A Place to Play: Experimentation and Interactions Between Technology and Performance' - Page 105 - Scott Palmer 9: 'Scenographic avant-gardes: Artistic Partnerships in Canada' - Page 121 - Natalie Rewa 10: 'Codes and Overloads: The Scenography of Richard Foreman' - Page 135 - Neal Swettenham 11: 'Spatial Practices: The Wooster Group's Rhode Island Trilogy' - Page 143 - Johann Callens 12: 'Physicality and Virtuality: Memory, Space and Actor on the Mediated Stage' - Page 157 - Thea Brejzek
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.071 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".