Bibliographic record
Abstract
This thesis examines the vocal aesthetics of sean-nós singing, the unaccompanied, unmetered Irish traditional singing genre that is rich in emotional depth and historical significance and is characterized by elegant melismatic melodies.Functioning as a pedagogical guide, this thesis offers insights into sean-nós singing techniques and Irish Lyric Diction. It begins with a historical overview of sean-nós singing and its relevance in contemporary pedagogy. Emphasizing the importance of understanding sean-nós within its historical context as a traditional art form of oppressed people in occupied Ireland, the thesis further addresses the appropriation of Irish music in Western repertoires and advocates for dialect-appropriate diction, and culturally informed performance and pedagogy. Accordingly, this dissertation takes a holistic approach, delving into the historical contexts of Irish art song in western lyric and sean-nós styles, analyzing the phonemes, articulation, and linguistic phenomena that comprise Lyric Diction, and examining the distinctive voice pedagogy necessary for sean-nós singing. Novel insights into the Connemara singing style identify previously unexplored features unique to that region. Elements of vocal function in sean-nós performance, including breathing, registration, vocal tract shaping, vibrato characteristics, phonation, and resonance strategies, including the genre-specific resonance strategy known onomatopoeically as neá, are examined and interpreted through the lens of Evidence-Based Voice Pedagogy to develop an understanding of optimal vocal outcomes in sean-nós styles. Recordings of first prize winning performances of the Corn Uí Riada “Ó Riada Cup” at the Oireachtas na Samhna “November Gathering” sean-nós singing competition, the ultimate honour in the world of sean-nós singing, are analyzed to identify common vocal factors among elite performers of sean-nós. The results of this inquiry inform the identification of vocal techniques specific to sean-nós singing, and provide strategies for achieving style-appropriate, efficient vocal production with linguistic integrity in sean-nós styles. The intent of this exploration is threefold: to support professional sean-nós singers and voice professionals in both teaching and performance; to foster authentic performance of sean-nós singing within the diaspora; and to enrich the body of culturally informed voice pedagogy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".