Art Songs of Charles Ives: Accessible to Beginning Singers
Bibliographic record
Abstract
abstract: The performance of Charles Ives's art songs can be challenging to even the most experienced singers, but to beginning singers, they may be even more so, due to such twentieth-century aspects as polytonality, polyrhythm, tone clusters, aleatoric elements, and quarter tones. However, Ives used previously existing material, often familiar hymn tunes, as the foundation for many of his art songs. If beginning students first are exposed to this borrowed material, such as a simple hymn tune, which should be well within even the most experienced singer's comfort range, they can then learn this tune first, as a more simplistic reference point, and then focus on how Ives altered the tunes, rather then having to learn what seems like an entirely new melody. In this way, Ives's art songs can become more accessible to less-experienced singers. This paper outlines a method for researching and learning the borrowed materials in Ives's songs that utilize them, and reviews materials already commonly used by voice teachers to help beginning students learn their music. By combining this method, which focuses on the borrowed materials, with standard practices teachers can then help their beginning students more easily learn and perform Ives's art songs. Four songs, from the set "Four Hymn Tune Settings" by Charles Ives are used to illustrate this method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".