Bibliographic record
Abstract
This article reevaluates critical descriptions of the discipline of music theory advanced by such scholars as Edward Cone, Joseph Kerman, and Leo Treitler and promoted in more recent assessments of theoretical work. In particular, it challenges critical conceptions of the field implying a binarism that casts music scholars either as “structuralist"—dependent upon preexisting analytical methodologies designed to derive musical meaning from the examination of the musical details of a work—or as “hermeneutic"—holding that musical meaning resides in the relationship of the musical composition to a particular historical or cultural circumstance. I argue that their conception of these two seemingly irreconcilable epistemological poles has led critical scholars to make certain assumptions that do not square with the practice of music research. In my evaluation of critical descriptions of the field of music, I turn to Thomas Kuhn's theory of scientific revolution as a corollary for critical claims about the development and growth of knowledge in the field of music and to Karl Popper, whose epistemological model seeks to address shortcomings perceived in Kuhn's theory. With reference to various analytical studies, I argue that Popper's model is superior to Kuhn's as a representation of the development of knowledge in the field of music theory and will thereby identify some of the problems inherent in critical descriptions of music analysis.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".