Narrative and representation in Robert Schumann's Waldszenen, Op. 82
Bibliographic record
Abstract
Robert Schumann's music is replete with literary references and extramusical indications. His devotion to literature and his adaptation of the narrative strategies of the early Romantics in his compositions have prompted many investigations of literary influences on Schumann's music. Many of his early piano cycles are inspired by the literature of the Romantics, and in particular by the novels of Jean Paul Richter. However, it has sometimes been suggested that Schumann discarded the narrative strategies of Jean Paul in his late compositions, some of which were written for musical education and music-making in the home. My goal, in this dissertation, is to demonstrate that Jean Paul's narrative devices remained relevant in Schumann's late works. This study examines the aspects of narrative and representation that permeate the Waldszenen cycle. The first aspect is large-cycle coherence, an effect that is achieved through innovative associational means -- including motivic and tonal cross-references-- and through more traditional hierarchical means, such as tonal departure and return and the use of programmatic titles that suggest a complete forest journey. The second aspect is the manipulation of formal conventions, which is accomplished through problematic closure, problematic recapitulation, and ambiguous formal function. The third aspect is the use of intertextual allusions to Schumann's earlier works. The last aspect of representation in Waldszenen is the use of three musical topics - fantasy, pastoral, and hunt - in association with their corresponding Romantic literary genres - Kunstmärchen, idyll, and hunting tale and song.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".