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Record W2894935556 · doi:10.30535/mto.24.3.2

Schubert’s Development of Harmonic Motives in his Early String Quartets

2018· article· en· W2894935556 on OpenAlexaff
Brian Black

Bibliographic record

VenueMusic Theory Online · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsString (physics)Period (music)HarmonicKey (lock)Classical periodLiteratureDevelopment (topology)HistoryTheoretical physicsArtCognitive sciencePsychologyAestheticsComputer sciencePhysicsMathematicsAcousticsMathematical analysis

Abstract

fetched live from OpenAlex

This essay looks at Schubert’s handling of harmonic motives in the first movements of four of his early string quartets dating from the period of approximately 1811 until late 1814, when he was fourteen to seventeen years old. Despite their many structural problems, these pieces provide an insight into Schubert’s development as a composer of sonata form. Even in the earliest of the examples studied, the young composer attempts to draw affective and structural consequences across the form from the first harmonic event of the music, which thus functions motivically in the unfolding of the movement. As his approach becomes increasingly flexible over the period under discussion, such harmonic motives become a dynamic force in the form, influencing tonal relations and modulatory strategies as well as looking forward to certain aspects of his mature three-key expositions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.008
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.053
GPT teacher head0.250
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations1
Published2018
Admission routes1
Has abstractyes

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