<i>BRANTWOOD:</i> Canada’s Largest Experiment in Immersive Theatre
Bibliographic record
Abstract
Julie Tepperman and Mitchell Cushman, co-creators, playwrights, and directors of Brantwood, write about the process of creating the largest site-specific immersive musical in Canada. Originally commissioned by Associate Dean Michael Rubinoff and Sheridan College’s Musical Theatre Program, Brantwood was designed for and performed in a 90-year-old school in Oakville, Ontario that had been closed down due to low enrolment. The piece consisted of 40 original songs (composed by Anika Johnson, Britta Johnson, and Bram Gielen) imbedded in 11 one-hour scripted storylines, each set during a different decade in the history of this imaginary high school. The 15 hours of total material unfolded simultaneously over the course of each three-hour performance, and the audience had free rein to roam everywhere in the building and to follow whichever character they chose in the 42 person cast. Julie and Mitchell write about the challenges of creating theatre on such a large scale, and immersing the audience in an environment that gives them freedom to roam and explore, and engage with the show in both direct and indirect ways, and to push the boundaries of intimacy between performer and audience.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".