Bibliographic record
Abstract
Choral singing…is inherently physical and innately personal, one of the most humanly intimate of all musical acts. It requires no mechanical intermediary, but clothes itself directly in our humanity. – Custer, 2001, p. 25. What does it mean for a choir to sing “in tune?” A plethora of choral methods, conducting, sight-singing, and ear-training texts provide practical, singing-based strategies for improving choral intonation and gesture-based strategies for improving conducting communication; but apart from aural/vocal and conducting methodology, traditional pedagogy fails to consider the human aspect of the choral singing equation. As a result the solutions presented–while in some cases immediately effective–may actually be only Band-Aid solutions to deeper ensemble problems. If a choir experiences intonation problems, lack of musicality, or ensemble failure, could it be that the singers do not care to sing in tune, musically, or as an ensemble? How can we move singers towards effective ensemble singing in a way that is nurturing, participant-centred, and permanently rooted in their musicianship? Following a consideration of philosophical and practical writings from the fields of choral music and music education, and a reflection on significant choral teaching endeavours and experiences with community and post-secondary ensembles, the author discusses
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".