Bibliographic record
Abstract
The relationship of jazz and theatre, as the articles in this issue demonstrate, is a complex one. The three reviews featured here speak to some of the diverse influences jazz, and the community it comes out of, has on contemporary theatre. Kate Bligh’s review discusses a collaborative work created for the Montreal International Jazz Festival by one of Canada’s best known men of the theatre, Robert Lepage, in collaboration with musician Peter Gabriel. Her description of the piece, which took place in a cabaret setting reminiscent of the early jazz scene, demonstrates the affinity of Lepage’s creation techniques with the jazz process of improvising to create multiple variations on well-known motifs. In the end though, Bligh wonders why Lepage’s use of these techniques yielded a series of images that, taken together, had only limited impact. Why, she asks, did the transitions between scenes sometimes seem more interesting than the scenes themselves? Was the technology overwhelming the liveness of the onstage performers? Or was there a more serious problem here, a problem that is perhaps evidenced in directorial choices that emphasized the formal qualities of the piece over its social meanings, as in a sequence where a monkey and a Zulu warrior seem to be presented as virtual stand-ins for each other?
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.164 | 0.157 |
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".