Bibliographic record
Abstract
When discussing theatre, most people will imagine a traditional audience-actor relationship, likely in a proscenium setting; the actors on stage inhabit the world of the story while audience members remain passive bystanders. Immersive theatre challenges this convention by bringing the audience into the world of the story. Comparable to being immersed in water, the audience members are fully submerged in this new world; senses are engaged and heightened, and curiosity is peaked. The audience becomes a part of the story world alongside the actors as the border between the real and the fictional becomes blurred. In the world of games and digital media, this border is known as the “magic circle”, a space with unique rules and values separate from day-to-day reality. Though this metaphor was created to describe a game space, it can be argued that audience members in immersive theatre performances have become “players” themselves. In this project, I explore the “play” aspect of immersive theatre, with a particular focus in the area of pervasive gaming. This project includes research from a variety of theatre practitioners, theorists, and game designers including Janet Murray, Josephine Machon, Katie Salen and Eric Zimmerman. Inspired by that research, I am creating a pervasive game designed to be played in and around the city of Kingston. The goal of this project is to further understand the ways in which theatre and gaming are intrinsically linked while experimenting with form and pushing the boundaries of the magic circle.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".