The Urge to Vary: Schubert's Variation Practice from Schubertiades to Sonata Forms
Bibliographic record
Abstract
The Urge to Vary: Schubert’s Variation Practice from Schubertiades to Sonata Forms Caitlin G. Martinkus Doctor of Philosophy, Music Theory Faculty of Music University of Toronto 2017 Abstract Repetition has long been a focal point of both critiques and scholarly inquiries of Schubert’s instrumental music. Casting repetition as redundant, critics condemned his instrumental music—especially those works in sonata forms—throughout the majority of the nineteenth- and twentieth-centuries. Recently, variation has emerged as a fruitful lens for the analysis of repetition in Schubert’s sonata forms. Building upon this notion, I further illuminate the role of variation (as a set of techniques, musical processes, and form) in Schubert’s idiom. To develop an analytical framework tailored to the analysis of variation, I establish why variation would be so prominent in Schubert’s oeuvre, and how it is employed. I lay the conceptual and analytical framework of the dissertation in Chapters 1 and 2. Chapter 1 establishes the centrality of repetition to discourse on Schubert and, through a thorough consideration of Schubert’s musical-social life, establishes the relevance of variation beyond composition. I also consider current tools of Formenlehre analysis, and situate my use of William Caplin’s theory of formal functions as a supplement to analyses of variation. Chapter 2 investigates idiomatic variation techniques. These techniques, set against the backdrop of Schubert’s musical training and practice, form the variation perspective that guides my analyses of sonata forms. In Chapters 3 and 4 I analyze the interaction between elements of variation and sonata form. I create my own analytical tools, including the concepts of embedded versus distributed variations, and tight-knit versus loose variation procedures, to demonstrate the presence of variation within and across large-scale units of musical form. Three full-movement case studies highlight Schubert’s use of variation (techniques and form) across expositions, developments, and recapitulations. Most pronounced in these analyses is Schubert’s use of variation in development sections. I thus close my analytical chapters by problematizing the binary opposition between thematic development and variation, for it is a distinction that lies at the heart of many critiques of Schubert’s sonata forms. Ultimately, this study reveals the many and nuanced ways in which elements of variation permeate Schubert’s oeuvre through a unique analytical framework, and situates Schubert’s sonata forms within the ever-evolving historical trajectory of the genre.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".