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Record W2085733270 · doi:10.1080/01411890108574793

Toward an epistemological model for music theory

2001· article· en· W2085733270 on OpenAlexaff
Karen Fournier

Bibliographic record

VenueJournal of Musicological Research · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsMusic theoryEpistemologyArtLiteraturePhilosophyMusical

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.625
GPT teacher head0.430
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2001
Admission routes1
Has abstractyes

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