Texture and Sonata Form in Classical String Quartets: A Corpus Study
Bibliographic record
Abstract
How does musical texture relate to large-scale form in classical string quartets? Are certain textural strategies associated with sections or formal functions in a sonata movement? Some music theorists have argued that contrapuntal textures are more common in developments and transitions. In their view, these medial sections would use polyphony to foster a sense of looseness, instability, and momentum. Our study tested these claims by examining a pre-existing corpus of string quartet movements in sonata form by Joseph Haydn, Wolfgang Amadeus Mozart, and Ludwig van Beethoven. We measured texture in terms of average onset synchrony, where lower onset synchrony represents greater rhythmic and textural independence among parts. Although average onset synchrony was lower in developments, compared to expositions, for most pieces in the corpus (65.22%), there was a significant interaction between section and composer, and post hoc analysis indicated that this difference in onset synchrony was significant only for Beethoven. Within expositions, transitions did not tend to have lower onset synchrony, and there was no significant effect for subsection. However, there was a significant main effect for composer here. Overall, these results imply that textural strategies in classical sonata form are complex and may vary from piece to piece and from composer to composer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".