Bibliographic record
Abstract
This article asks questions about scale and the writing of musical theatre. Do considerations of being “produce-able” (particularly in the current Canadian theatre ecosystem) affect how writers approach their work? Often, making a new work “produce-able” involves removing any extraneous elements that would create additional costs. This has resulted in a litany of Canadian plays that have small casts, unit sets, and contemporary (read: inexpensive) costumes. Yet, when one considers the issue of produce-ability within the context of musical theatre, there are many more things to consider, including a number of musicians, variety and quantity of singing voices, scope of orchestration, and variety of instrumentation. Writer-Composer Rob Kempson asks a selection of other Canadian musical theatre writers for their thoughts on these questions, and discovers that there is a divide around how the issues of scale and “produce-ability” influences their work. Composers interviewed include Jay Turvey & Paul Sportelli, Jim Betts, Brian Hill & Neil Bartram, Wesley J. Colford, and Scott Christian.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".