JABBLE! Choral Improvisation: A Model of Shared Leadership
Bibliographic record
Abstract
Is choral singing merely a process of compliant singers taking directions from a choral expert in the interest of eliminating mistakes and polishing repertoire for public presentation?We read that the power of group singing is essentially a social phenomenon.Durrant (2000) concludes that the conductor has a "critical role in enabling social cohesion and emotional catharsis as well as developing musical skills in choral singing."(p.84) Along with the social phenomenon of singing, conventional practice reinforces the conductor as the one who focuses the event.We explore this role in light of developing a choral improvisational intelligence, and explore the processes that culminated in a Wilfrid Laurier University Choir Concert that was based on student improvisation.The residency of Dr. Peter Wiegold at Wilfrid Laurier University in October of 2011, as part of a funded research project, provided the spark and the resources for building a choral concert on improvisation.Jabble explored the use of embedded improvisation within precomposed works as well as processes of free and pre-structured improvisation using musical embryos known as "backbones," originally pioneered by Weigold.The study indicated fundamental changes in the conventional choral leadership paradigm in the context of choral improvisation.Within the choral improvisation process conductors took on the role of musical facilitators and editors, whereas choristers became increasingly responsible for both basic and creative musical decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".