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Record W1535363620 · doi:10.7202/1016198ar

The human and the physical in Debussy’s depictions of snow

2013· article· en· W1535363620 on OpenAlexvenueno aff
Michael Oravitz

Bibliographic record

VenueLes Cahiers de la Société québécoise de recherche en musique · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeContemplationArtLiteratureAestheticsVisual artsPhilosophy

Abstract

fetched live from OpenAlex

This article explores Debussy’s musical engagement of the topic of snow in two representative works for piano, “Des pas sur la neige” from Préludes , Book I and “The Snow is Dancing” from Children’s Corner . In the analysis of “Des pas,” a narrative is put forth based on the interpretation of the work’s opening short-long rhythmic figure as a representation of contact with a frozen, encrusted surface followed by an immediate collapse of that frozen surface and contact with the ground underneath. As the work’s protagonist engages in walking meditation to confront a troubling memory, a state of contemplation is achieved whereby the musical portrayal of the footsteps are momentarily suppressed in strategic portions of the work designed to portray recollection, as Steven Rings (2008) has noted. In my analysis, I argue that the closing measures’ portrayal of footsteps is shifted into binary alterations of two notes, D and G , in steady quarter notes, so as to illustrate the protagonist’s emergence from an off beaten path to a more trodden one. In “The Snow is Dancing,” Debussy shifts focus from that of human emotion to the actual physical properties of snow. Interestingly, the snow is anthropomorphized nonetheless into dancers. I trace shifts in rhythm, line and register, and engage gestures of motion to illustrate Debussy’s compositional approach to both gravitationally suspending and animating wind-driven snowflakes.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.046
GPT teacher head0.322
Teacher spread0.276 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

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