Bibliographic record
Abstract
Argentine art song is a branch of the art song genre that is virtually unknown to Canadian academics and performers. Although there are numerous composers who have contributed songs to this genre, these composers and their contributions to the body of art song repertoire remain virtually unknown. The illusion of their non-existence is perpetuated by a cycle of factors. Publishers, not interested in publishing a product for which there is limited interest, print very little Argentine music. The lack of musical scores makes it difficult for performers to program and record this music. Thus, very few people are exposed to it, and most people continue to know very little about this rich body of repertoire that has much to offer performers and voice pedagogues.\nThe goal of this study of Argentine art song is to introduce this repertoire to pedagogues and performers and inform them of some of the challenges and benefits of incorporating this music into the standard art song repertoire. Many of the songs need “cultural translation” for those unfamiliar with the gaucho and other aspects of Argentine music, history, and culture. The gaucho and his influence on Argentine art song, both musically and thematically, is examined so that meaningful and informed performances can be prepared. Also, the pedagogical value of the repertoire in the voice studio is also discussed so that pedagogues can make informed decisions regarding its best application in the voice studio. An added benefit is that these songs introduce a greater variety of material and expression to the voice studio and recital stage that will make them welcome additions to the standard art song repertoire.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.021 |
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".