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Record W4392336259 · doi:10.1515/9781782049180

The Dawn of Music Semiology

2017· book· en· W4392336259 on OpenAlexaboutno aff

Bibliographic record

VenueBoydell and Brewer eBooks · 2017
Typebook
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSemiologyArtPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Showcases the energy and diversity of the young field of music semiology, appealing to readers who want to explore the meaning of music in our lives. The Dawn of Music Semiology showcases the work of nine leading musicologists, inspired by the work of Jean-Jacques Nattiez, the founding father of music semiology. Now entering its fifth decade as Nattiez enters his eighth,music semiology, or music semiotics, is still a young, vibrant field, and this book reflects its energy and diversity. It appeals to readers wanting to explore the meaning of music in our lives and to understand the ways of appreciating the complexities that lie behind its simple beauty and direct impact on us. Following a preface by Pierre Boulez and an introduction by the editors, nine chapters discuss the latest thinking about general considerations such as music and gesture, the psychology of music, and the role of ethnotheory. The volume offers new research on topics as diverse as modeling folk polyphony, spatialization in the Darmstadt repertoire, Schenker's theory of musical content, compositional modernism from Wagner to Boulez, current music theory terminology, and Maderna's use of folk music in serial composition. CONTRIBUTORS: Kofi Agawu, Simha Arom, Rossana Dalmonte, Irène Deliège, Jonathan Dunsby, Jonathan Goldman, Nicolas Meeùs, Jean Molino, Arnold Whittall Jonathan Dunsby is Professor of Music Theory at the Eastman School of Music, University of Rochester. Jonathan Goldman is Professor of Musicology at the University of Montreal.

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 score0.683
Threshold uncertainty score0.568

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.025
GPT teacher head0.231
Teacher spread0.206 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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