Bibliographic record
Abstract
That performance can be captured and replayed may be a given, but much less well-understood is the relationship between that stored performance, the memory of a performed act, and the live event. The practice of intermedial theatre offers a unique way to probe that relationship, and the awareness of metatheatre further exposes the links between memory, technology, and performance. This paper argues that intermedial metatheatre, and my own work as a director, video-maker, and video performer for a 2014 production of Daniel MacIvor’s play Never Swim Alone, provides a valuable model for research-creation that can shed light on these questions in an embodied and experiential manner. Engaging with Sarah Kember and Joanna Zylisnka’s notion of performative media, as well as the concept of memory work from Annette Kuhn, I offer thoughts on materiality, time, and mediated memory, captured through technologies like camera and sound recording equipment, and in constructed spaces like studios and rehearsal rooms, represented by projection mapping software and digital projectors in the theatre space. All these are integrated into the network of actors and techniques that make up current practices in intermedial theatrical creation. Can this research begin to unravel ideas of authenticity and liveness when reactivating memories in relation to live actors, and before an audience? And what of our responsibility to the digital representation and the live performer when bringing these memories and moments together? How does the network of technologies and practitioners in intermedial theatre creation and presentation, understand and respect the “lifeness” of a mediated memory?
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".