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Record W7054909595

The Art Songs of Violet Archer: A Performer's Analysis and Perspective on Repertoire for the Mezzo-soprano Voice

2022· dissertation· en· W7054909595 on OpenAlexaboutno aff

Bibliographic record

VenueTSpace (University of Toronto) · 2022
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsnot available
Fundersnot available
KeywordsRepertoirePerspective (graphical)MusicalPeriod (music)GuitarSingingIdentity (music)Key (lock)
DOInot available

Abstract

fetched live from OpenAlex

This thesis offers an in-depth look at the art song repertoire of Violet Archer by focusing on her compositions for the mezzo-soprano voice and piano. Factors that influenced this distinctive body of music and recognizes the important role that Violet Archer played as a composer in Canada during the twentieth century will be considered. My own introduction to Archer came during my undergraduate studies while taking a course on Canadian music, however until recently I had heard very little of her works performed, programed, or studied, but was drawn to her story of resilience and perseverance to pave her own path as a prominent composer and educator.\n\tThe goal of this research was to explore selected works from this repertoire to look at its suitability for singers at differing levels, taking into consideration musical elements such as harmony, melody, text, and rhythmic setting. This necessitated a literature review to determine how vocal compositions are assessed within the fields of voice pedagogy and performance, and to examine resources that looked at Violet Archer’s music from a compositional standpoint. It also merited interviews conducted with individuals who had worked with Archer directly on some of her vocal compositions. Each song analyzed for this study was assessed according to three levels of difficulty (minimal, moderate, and extensive) resulting in a numeric amount which corresponded to the piece being classified as elementary, intermediate, or advanced. The literature review helped to inform my approach to this repertoire selection and to establish a grid with which to classify these works in order to provide a context for analyses and further discussion on each piece. Repertoire selected included songs and song cycles that have very little or no attention given to them in prior scholarly articles and research. My hope is that a detailed exploration of this selected repertoire will encourage singers and teachers alike to engage in the full breadth of what Violet Archer has to offer us as performers, teachers, and lovers of music.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.995

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)0.0060.000

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 head0.006
GPT teacher head0.268
Teacher spread0.261 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

Explore more

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