Caitlin Vincent, <i>Digital Scenography in the Twenty-First Century</i> , Ashgate Interdisciplinary Studies in Opera - Caitlin Vincent, <i>Digital Scenography in the Twenty-First Century</i> , Ashgate Interdisciplinary Studies in Opera Abingdon: Routledge, 2022. 212 pp. ISBN 9780367553920 (hard cover). DOI: 10.4324/9781003093305 (ebook)
Bibliographic record
Abstract
It is June 2022 and the premiere of R.U.R. Torrent of Light , a multimedia opera, in Toronto is enthralling audiences. Inspired by Karel Čapek’s play that introduced the word ‘robot’ into 20th-century vocabulary, composer Nicole Lizée and librettist and playwright Nicholas Billon have conceived a science-fiction opera for our current fascination with AI (artificial intelligence). The production by Toronto’s Tapestry Opera Theatre is in partnership with the research and creation of new technologies at the Social Body Lab and the Digital Futures Initiatives at Ontario College of Art & Design University. It has been conceived to embed technologies into the scenographic dramaturgy as projections, wearable art and spatialisation of sound and light through the costuming and neologisms in instrumentation to create a ‘unique electronica-classical sound’ of the neo-futurist worlding. After the hiatus of the last two years, this live performance highlights our growing recognition of scenographic vocabularies of programmable performance affecting behaviours, as well as expanded definitions of sound and digital integration as contemporary opera. Such interdisciplinarity has often brought the spectator into fresh relationships with opera in performance, thereby activating the event of spectatorship.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".