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Record W3157063339 · doi:10.13023/etd.2021.010

The G7 Suite

2021· article· en· W3157063339 on OpenAlexaboutno aff
Joseph Dunn

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSuiteComputer scienceArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

The G7 Suite is a multi-movement chamber work that combines elements of European Art Music, Indigenous Music from Latin America, and various representations of American music. The melodic material is derived from the national anthems of the Great Seven nations: Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. Each melody is re-set to new music genres and aligns itself to the rules and expectations within each idiom. This compilation is more than a series of arrangements or reharmonizations of the anthems; these are new compositions based on melodic elements from previous works. This analysis of The G7 Suite will serve three primary functions. First, each analysis will engage the sociological and/or cultural relevance of each nation or idiom. Essentially, the composer offers perspective on the relationship between the nation and the new idiom within each movement. Second, the idiomatic characteristics of each genre are revealed to and discussed with the reader. Here, the reader is able to quantify and qualify the blended characteristics of the source material and the new idiom. Last, attention will be focused on the relationships between the source material and the new composition: this is the meat of the analysis. Care will be taken to examine the interaction of the anthems and the idioms while relating each movement to the broader G7 Suite.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.996

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.017
GPT teacher head0.164
Teacher spread0.148 · 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 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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