Bibliographic record
Abstract
This article investigates the emerging methodologies of interactive performance. Interactive performance has been defined as one in which: digital technology is a central strategy, and there is a corporeal presence (live body) on stage, and there is real-time interactivity between the two. The central question of this study revolves around the evolving systems used to develop this work, specifically searching for solutions to the complex challenges raised when developing digital tools synergistically with dramatic content. As artistic researchers and educators in the School of the Arts, Media, Performance and Design, York University, and Artistic Directors of Out of the Box Productions, Gwenyth Dobie and William Mackwood are advantageously positioned to use creative practice as a primary means and method of inquiry. Through their research-creation work they recognize the general term “Animator” as better able to describe the blend of knowledge, interests, and investment needed of all participants for a successful outcome. Further, they implement the terms “Animator Performer” (AP) and “Media Animator” (MA) when working in the world of interactive performance. From the findings on their most recent immersive piece Rallentando — an Installation, Dobie and Mackwood detail new creation methodologies where Media Animator/Animator Performer collaborate in ‘real-time’ throughout the developmental process.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".