Bibliographic record
Abstract
When I started teaching in the theatre program at Humber College in 1998, I had never heard the term “devised theatre.” We talked vaguely about “physical theatre” or “creation-based training,” and even then most people didn’t know what we were talking about. Was it Lecoq? Was it Grotowski? Was it collective creation à la Passe Muraille? Where did it come from and why would anyone want or need to train in it? My understanding of this vaguely named, and even more vaguely defined, approach was rooted in the work of Primus Theatre in Winnipeg, fed by a number of productions that surfaced occasionally at Toronto’s World Stage Festival and wrestled with over long conversations (and bottles of Irish whiskey) with Richard Fowler. Devised theatre became a term applied to productions — but where was the training that made it possible? Was it only for artists of seemingly superior intellectual capacity or did it contain something that could speak to a wider and more disparate community? Somewhere in the mists of my brain an idea started to form that would bring several of my passionate beliefs about contemporary theatre training together.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".