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Expressive Functions of Variation in the German Lied

2025· book-chapter· en· W4414431353 on OpenAlexaff
Harald Krebs

Bibliographic record

VenueOxford University Press eBooks · 2025
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLinguistics and language evolution
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVariation (astronomy)RhythmPianoGermanExpression (computer science)Poetry

Abstract

fetched live from OpenAlex

Abstract Variation technique occurs frequently in vocal genres, including German Lieder. In the latter genre, it may occur in any situation involving immediate or non-immediate repetition: in contiguous or non-contiguous restatements of short segments; in the contiguous sections of strophic forms; and in non-contiguous restatements of substantial sections in forms such as ternary or rondo (the former is much more common in Lieder than the latter). Variation technique in Lieder occurs in various ways: as rhythmic variation of an earlier passage in the piano part alone, with an unaltered vocal line; as rhythmic variation in the vocal line, with an unaltered or varied piano part; and as extensive recomposition of the vocal line (in terms of rhythm, pitch, or both), with an unaltered or varied piano part. Variation technique contributes to text expression in numerous ways. Even minor, momentary deviations within a restatement of earlier material may play an expressive role by highlighting a significant word. More extensive alterations may have more profound expressive consequences. The composer might coordinate the greater, or more complex, rhythmic activity in a varied passage with some form of intensification within the poem, or might craft a variation in such a way that it “paints” the imagery of the poetic text at the given point. Examples are drawn from Lieder by numerous nineteenth-century composers, with emphasis on songs by women (Josephine Lang, Fanny Hensel, and Clara Schumann).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.991
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.200
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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