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Record W4392089105 · doi:10.1017/9781800108929.006

By Way of Conclusion: Unity and Analytical Paradigms

2023· other· en· W4392089105 on OpenAlexaff
Steven Huebner

Bibliographic record

Venuenot available
Typeother
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Unity and Analytical Paradigms Theoretical tools designed to show unity have long been used implicitly to substantiate the integrity of composers (and theorists) faced with the commodification of culture, functioning as tropes for creative authority and autonomy, and a ready criterion for value judgment. In the wake of Romantic aesthetics that critiqued conventional harmonic syntax and stereotypical melodic figures as a basis for coherence, such tools became particularly urgent. “Everyone knows,” wrote Roger Parker over thirty years ago in a study of motivic development in Aida , “[that] in good music a search for ‘motivic coherence’ will almost always be rewarded in overabundance.” These kinds of remarks have most often been tested with reference to German repertory extending from Bach to Wagner and beyond. But for quite some time now, the wall erected by critics such as Arnold Schoenberg and Carl Dahlhaus between works that putatively follow their own internal designs and those beholden to social practice and institutions (such as nineteenth-century Italian opera) has crumbled, not only because the former have been seen as grounded in ideology and society but also because the latter have been subjected to a new and wide variety of theoretical tests of what constitutes “good music.” Varieties of Unity Yet although the new apparatus of music theory has modernized Verdi research—just as productions of the works themselves have been modernized—it has come under fire over a range of concerns. Quite some time ago, the houselights dimmed on the type of musical logic that privileges motivic and tonal relationships. Some have objected to a tendency to “terrorize” historical Others and crush figures of the past with the heavy armor of modern analytical techniques, others to a plethora of graphs and charts and tedious prose. These complaints have been effective in encouraging greater nuance in critical and analytical discussions but often do not seem to foster methodological pluralism any more than their targets. We would do well to remember that methodologies are the product not only of ideologies, authority structures, and/or consensus-seeking communities but of personal temperaments as well. One prominent suggestion that opera criticism should forgo analyses that seek coherence in unity came from James Webster in the late 1980s. He admonished readers “to conduct our searches for tonal coherence as skeptically as we know how, and to accept from the beginning and without bias the possibility that we may not find it.”

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0070.044
Scholarly communication0.0200.027
Open science0.0030.009
Research integrity0.0060.015
Insufficient payload (model declined to judge)0.0130.003

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 head0.031
GPT teacher head0.240
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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