Bibliographic record
Abstract
his project explores the creative process of making physical theatre. I am exploring the creation of physical performance ‘texts’ that respond to a play script, but that do not incorporate spoken word as part of the storytelling. Up until now, my experience with theatre at Queen’s has been mostly centered around the spoken word as the primary mode of storytelling. Even when that script has been a musical, complete with choreography and vocals, the process has still been largely centered around the spoken (or sung) text. With this research project, I am exploring storytelling in theatre through movement. I am experimenting with creating a physical theatre narrative, inspired by a previously published script (Lilies by Michel Marc Bouchard), but not entirely driven by the spoken word in that text. This project includes concentrated research on noted physical theatre theorists such as Jacques Lecoq and Philip Gaulier, as well as on prominent physical theatre companies around the globe. Inspired by that research, I am workshopping a short piece of physical theatre. I will report on my experiences experimenting with creating a physicalized text in the rehearsal hall. The goal of this project isn’t about removing or disregarding the text, but is instead it to use what is given, and perform it through a different medium of theatrical communication: the physical body.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".