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Record W2806295580 · doi:10.30535/mto.17.3.11

Review of Danuta Mirka,<i>Metric Manipulations in Haydn and Mozart: Chamber Music for Strings, 1787–1791</i>(Oxford University Press, 2009)

2011· article· en· W2806295580 on OpenAlexaff
Harald Krebs

Bibliographic record

VenueMusic Theory Online · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCounterpointMusicalMetric (unit)ConsciousnessCognitive scienceComputer sciencePerceptionLinguisticsArtVisual artsPhilosophyEpistemologyPsychology

Abstract

fetched live from OpenAlex

Danuta Mirka's Metric Manipulations is a welcome addition to the impressive body of work on rhythm and meter that our field has produced during the past few decades-welcome especially because it brings eighteenth-century theories of rhythm and meter more fully into the current discussion than do most earlier volumes.Like William Rothstein in Phrase Rhythm in Tonal Music, a book to which she refers as "one of the most important catalysts of [her] study" (xi), Mirka fashions an intricate counterpoint of eighteenth-century thought and more recent ideas.[2] She begins to weave this counterpoint in the first chapter ("Musical Meter between Composition and Perception"), in which she explains the hierarchical metric theories of Kirnberger, Schulz and Koch, then links these to the more recent hierarchical theories of Cooper and Meyer, Yeston, and Lerdahl and Jackendoff. (1) From these hierarchical models, she then turns toward dynamic, perception-based models of meter (a dynamic model being necessary for her later analyses of metric manipulations).Christopher Hasty's theories assume prominence here, but they are blended with those of Jackendoff-not the Jackendoff of A Generative Theory of Tonal Music (1983), but of Consciousness and the Computational Mind (1987); Mirka's analytical approach combines Hasty's idea of projection with Jackendoff 's linguistics-inspired notion of a "parallel multipleanalysis" processor. (2)Mirka's processor models listeners' metric perceptions by busily gathering information from a given musical surface and, on the basis of this information, selecting possible metric organizations.Being a "multiple-analysis" processor, it is prepared to fluctuate between conflicting metric interpretations when confronted with complex contexts.The Hasty-Jackendoff fusion is evident in Mirka's musical examples; under many of the score excerpts, one finds hierarchical dot diagrams, but the dots are joined by projective arrows, à la Hasty.[3] Having introduced her processor, Mirka puts it to work in the analysis of eighteenth-century music.She wisely chooses to focus on a circumscribed body of eighteenth-century repertoire-on two composers (Haydn and Mozart), on one medium (music for strings), and on a five-year span (1787-91).A wealth of wonderful music and, specifically, of metric complexities is included within her chosen limits; there is plenty of grist for the processor's mill.In Chapters 2-6, Mirka describes the

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

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

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 head0.117
GPT teacher head0.284
Teacher spread0.167 · 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.

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

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Citations0
Published2011
Admission routes1
Has abstractyes

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