Bibliographic record
Abstract
Traditional vocal music is a valuable resource for aural skills instruction. As undergradaute aural skills curricula typically focus on music of the so-called “Common Practice Period ” and aspects of 20th and 21st century music, folk songs offer a nice counter-balance to an otherwise predominantly Western Fine Art Music-focused repertoire. Having served as an aural skills graduate teaching assistant for Gary S. Karpinski at the University of Massachusetts Amherst, I was impressed by his curriculum and the diverse music sources that he drew from to teach sight-singing skills. As some of the repertoire in-cluded folk songs from many parts of the world, in-cluding Canada, and more specifically Nova Scotia, I was inspired to consider how broadly Nova Scotian songs could be integrated within the framework of his undergraduate curriculum. The music of Canada is already being taught to an incresingly global audience through the adoption of various Canadian provincial Department of Educa-tion curricula in international K-12 schools around the world. Although I teach at the unviersity level, I also want to promote Canadian music, whenever possible. Assessing music for its potential pedagogical val-ue can be a lengthy and time-consuming ordeal. As such, many instructors will rely on anthologies of music compiled by devoted aural skills instructors who have devoted years (and decades) of their life to this task. Repertoire for collegiate sight-singing an-thologies draw chiefly from either preexisting sources or specifically composed music. Examples of sight-singing anthologies relying on “synthetic ” or pedagogically focused compositions include Benja-
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.133 | 0.056 |
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".