Review of Danuta Mirka,<i>Metric Manipulations in Haydn and Mozart: Chamber Music for Strings, 1787–1791</i>(Oxford University Press, 2009)
Bibliographic record
Abstract
Danuta Mirka'sMetric 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
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".