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Record W4392336706 · doi:10.1515/9781782045977

Formal Functions in Perspective

2015· book· en· W4392336706 on OpenAlexaboutno aff

Bibliographic record

VenueBoydell and Brewer eBooks · 2015
Typebook
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Presents thirteen studies that engage with the notion of formal function in a variety of ways Among the more striking developments in contemporary North American music theory is the renewed centrality of issues of musical form (Formenlehre) . Formal Functions in Perspective presents thirteen studies that engage with musical form in a variety of ways. The essays, written by established and emerging scholars from the United States, the United Kingdom, Canada, and the European continent, run the chronological gamut from Haydn and Clementito Leibowitz and Adorno; they discuss Lieder , arias, and choral music as well as symphonies, concerti, and chamber works; they treat Haydn's humor and Saint-Saëns's politics, while discussions of particular pieces range from Mozart's arias to Schoenberg's Verklärte Nacht . Running through the essays and connecting them thematically is the central notion of formal function. CONTRIBUTORS: Brian Black, L. Poundie Burstein, Andrew Deruchie, Julian Horton, Steven Huebner, Harald Krebs, Henry Klumpenhouwer, Nathan John Martin, François de Médicis, Christoph Neidhöfer, Julie Pedneault-Deslauriers, Giorgio Sanguinetti, Janet Schmalfeldt, Peter Schubert, Steven Vande Moortele Steven Vande Moortele is assistant professor of music theory at the University of Toronto. Julie Pedneault-Deslauriers is assistant professor of music at the University of Ottawa. Nathan John Martin is assistant professor of music at the University of Michigan.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.014
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations31
Published2015
Admission routes1
Has abstractyes

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