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Record W2790051568 · doi:10.14288/1.0308601

African and western aspects of Ballanta's opera "Afiwa"

2016· article· en· W2790051568 on OpenAlexaff
Joseryl Olayinka Lucy Beckley

Bibliographic record

VenuecIRcle (University of British Columbia) · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHistoryArt

Abstract

fetched live from OpenAlex

Nicholas George Julius Ballanta (1893-1962) was a Sierra Leonean composer, ethnomusicologist and scholar. Trained in Western art and church music both in his home country and in the United States, he also conducted many years’ research into tribal music in Africa, and indeed was a pioneer in the study of West African music. His most complete opera is “Afiwa,” which sets the story of a girl who stands up to her father, the king of the Anlo Ewe tribe in Ghana, for atrocities he had committed at her birth. This study identifies what is uniquely African yet also Western about Ballanta’s “Afiwa”. In chapter 1, an introduction to the work is presented, including Ballanta’s biography. In Chapter 2, I determine what Ballanta believed to be characteristic of the African music he studied by examining his writings about rhythm, melody, form, texture and harmony. In Chapter 3, I cite numerous passages from “Afiwa” where these characteristics are found. My conclusion is that Ballanta combined both African and Western musical aspects in this opera. Chapter 4 goes beyond the music to explain, referencing the 2010 Cottey College production, aspects of the libretto, plot and staging that will help any future producers to understand the opera better so as to provide as authentic a production as possible.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.158
Teacher spread0.147 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2016
Admission routes1
Has abstractyes

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