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Record W4236686017 · doi:10.1017/cbo9780511843068

Music Sketches

2015· book· en· W4236686017 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSketchMusicalVariety (cybernetics)AutographSelection (genetic algorithm)Computer scienceVisual artsArtLiteratureArtificial intelligence

Abstract

fetched live from OpenAlex

The term 'music sketch' relates to the vast variety of documents that are used by composers to work out a musical technique or idea and to prepare their work for performance or publication. These documents can often provide crucial insights into authorship, biography, editorial practice and musical analysis. This introduction provides students and scholars with the knowledge and skills they need to embark on research projects involving the study of composers' working documents. Presenting examples of the compositional process over a 400-year period, it includes a selection of detailed case studies on how sketches were created and the techniques that were used, such as transcription and the sorting of loose leaves. Numerous illustrations of manuscripts and autographs, many of which have never been published before, show how these vital documents can be used to better understand compositional processes.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.497
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.193
Teacher spread0.133 · 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