Bibliographic record
Abstract
Singing is a powerful human activity. The intentional use of our body to create musical sounds is an intensely felt physical, spiritual and cognitive pursuit. When people sing songs, even at the most beginning level, they are drawing on an incredible amount of what David Elliott has characterized as "procedural knowledge" or knowing-in-action (Elliott, 1993, 1995). Procedural knowledge is one component of musicianship, a highly complex, multidimensional form of knowledge that develops over the course of an individual's experience with music. Access to her singing voice is not only every student's right, it is also central to the development of musicianship (Rao, 1997). Singing is a key way that individuals develop and demonstrate their musical knowledge, their musicianship. But singing is more than an individual aurally demonstrating her musicianship. Recent research in music education stresses the multi-dimensional nature of music making as both an aural/physical phenomenon and a social one (Bowman, I993a, p. 55). The sounds touch us and the social nature binds us in community. To think of one feature without the other is to miss an essential characteristic of what makes music "music", and yet so often we concentrate on how to improve our production of the sound qualities of music, without looking at the concomitant social effects of making music together. Music education philosopher Wayne Bowman has eloquently linked the two.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".