Bibliographic record
Abstract
In the course of the introductory commentary on hypermeter in A Generative Theory of Tonal Music (GTTM), Lerdahl and Jackendoff discuss the opening measures of Mozart’s Symphony No. 40 in G Minor, a hypermetrically ambiguous passage in which “the performer’s choice . . . can tip the balance one way or the other for the listener.” Through reflections on concepts from more recent psychological inquiry into performance, and on the interpretations of the passage that are projected in four well-known recordings of the Symphony, I will develop a set of theoretical principles that describe the “balance-tipping” effects of performance-specific elements on hypermetric structures inferred by the listener. This special case will lead to a more general reconsideration of the place of performance in the design of the Lerdahl-Jackendoff theory. The article proceeds in five parts: (1) an introduction to the main theoretical concepts to be discussed, including a brief consideration of current debates with which the study intersects; (2) a critical discussion of the relationship between hypermeter and performance that is proposed in GTTM; (3) an attempt at extending the theory of accent types to include a special class of phenomenal accents that is under the performer’s control; (4) a close reading of four recordings, facilitated by quantitative performance analyses, and an attempt at explaining their hypermetric patterns as transformations of perfectly regular underlying structures; and (5) concluding remarks of a more general nature on the relationship between structure and performance in GTTM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".