Hypermetrical Irregularity in Sonata Form: A Corpus Study
Bibliographic record
Abstract
In sonata form, development sections are characterized by tonal, textural, and phrase-structural instability. But are these instabilities counterbalanced by regularity in other musical domains? Are any syntactic layers more consistent in developments, relative to expositions or recapitulations? This corpus study examined hypermeter in expositions and developments from eighteenth- and nineteenth-century symphonic sonata movements. It analyzed both hypermetrical shifts (where a hypermeasure's duration differs from that of the preceding group) and hypermetrical deviations (where a hypermeasure departs from the four-measure norm). Developments had significantly less hypermetrical irregularity than expositions. This difference between formal sections was observed with all composers in the corpus, though they used varied amounts of hypermetrical regularity overall. These results, which are likely related to sequence blocks in the developmental core, suggest that hypermetrical grouping might serve a stabilizing function in sonata developments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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; both teacher heads agree on what is shown here.
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".