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Record W4316657342 · doi:10.1017/9781800101944.007

Chopin

2021· other· en· W4316657342 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

On the piano, playing Chopin in a tasteful way requires one to walk a tightrope, with tasteless over-romanticism on one side, and square and sterile academia on the other. On the Pleyel, this tightrope expanded a bit, and there was some ease and comfort in interpretation. —a student in my seminar at the Université de Montréal Chopin on the Pleyel The Pleyel brings us closer to Chopin. His affinity for these pianos is well-documented;he used them for his Paris performances whenever possible and also in his teaching. They can help us interpret his highly prescriptive notation, now available in new, more accurate critical editions (such as Jan Ekier’s for the Polish National Edition, and John Rink et al. for Peters). Accounts of his playing and teaching—conveniently organized and indexed by Eigeldinger—provide further insight into Chopin’s style, as do recordings by pianists descended from the Chopin circle such as Raoul Koczalski, Raoul Pugno, and Moriz Rosenthal. The new editions include alternate readings for many passages.These variants complicate the editor’s job but are a treasure trove for interpreters: besides giving us choices of what to play, they remind us that this music was born of improvisation. Yet despite their improvisatory origins, Chopin’s scores are astonishingly specific, with meticulous instructions for dynamics, articulation, and pedaling. These contradictory traits of freedom and control are beautifully reflected in this description of Chopin’s compositional process by his companion George Sand: His creativity was spontaneous, miraculous; he found it without seeking it, without expecting it. It arrived at his piano suddenly, completely, sublimely, or it sang in his head during a walk, and he would hasten to hear it again by recreating it on his instrument. But then would begin the most heartbreaking labor I have ever witnessed … He would shut himself up in his room for days at a time, weeping, pacing, breaking his pens, repeating or changing a single measure a hundred times, writing it and erasing it with equal frequency, and beginning again the next day with desperate perseverance.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.544
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5440.337

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 head0.039
GPT teacher head0.224
Teacher spread0.185 · 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
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
Published2021
Admission routes1
Has abstractyes

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