Bibliographic record
Abstract
According to Plato and Aristotle, the word ethos referred to whatever expressed or imitated the inner life of human beings, and music was required to reflect this ethos. Plato must have simultaneously intuited a chaos that cannot be verbalized while searching to explain why it was necessary to imitate the joy, anger, sorrow, and pleasure that exist in human beings, consequently, Plato expelled artists from his Republic. Aristotle, on the other hand, defended art because he found a reason for its existence, namely, its utility as therapy. However, both adhered to art imitation theory, or so-called mimesis . In other words, art was explained by logos , which requires a reason for existence (e.g., Imada, 2012 ). Plato&s;s and Aristotle&s;s theories of art have survived through various interpretations and translations from Roman times to modern Europe. However, this European concept of mimesis isolated music from the earliest experience of it and eventually created the separation of form and content in art (e.g., Sontag, 1990 ). This chapter critiques the way in which this separation of form (as sonorous air) and content (storyline) actually plays out in terms of the activity of creative music making and examines its recommendations for music education, referring to a “sound project” based on the concept of “soundscape” by the Canadian composer R. Murray Schafer at a secondary school in Hirosaki, Japan.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.439 | 0.272 |
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".