Bibliographic record
Abstract
As digital technology progresses, it increasingly mediates human interaction. Simple discussion has shifted from occurring only in person to being mediated by telephone, texting, video calling, Twitter, Facebook and a myriad of other technologies and services. Likewise, theatre has been undergoing a similar shift from an art form that only occurs 'in person' to one in which technology often mediates presence. In his book Liveness, Philip Auslander traces the roots of digital mediation back to the advent of television and the resulting cycle of reinterpretation, or remediation as it is termed by Bolter and Grusin, of different art mediums within one another. Innovative Canadian artists Robert Lepage and Kim Collier are currently engaging in the remediation of traditional art mediums on the stage by taking a distinctly cinematic approach to theatre. This study intends to evaluate the remediation of these mediums both in the theatre and in live performances such as sporting events. It will then consider current trends in integrating interactive ‘new’ media into live and pre-recorded events, and how these ‘new’ media may already be manifesting themselves elsewhere via remediation. This discussion will give special consideration to immersive theatre, in which audiences are free to navigate theatrical space autonomously and observe as they wish. Key questions to be considered include: What are the tools of mediation, and what are their effects? How might digital (re)mediation be reinventing the way we tell and receive stories in the theatre? In what ways can the theatre further reinterpret ‘new’ interactive media?
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".