Voicing Culture: Training Korean Actors' Voices through the Namdaemun Market Projects
Bibliographic record
Abstract
Over the past ten years, scholars and voice practitioners have debated issues surrounding the training of the actor's voice and addressed the actor's culture during that training. This article asks, What cultural assumptions are embedded within mainstream voice approaches and how are these transmitted within classrooms that are becoming increasingly multicultural/multilinguistic? How might re-contextualizing western voice praxis in a Korean acting conservatoire reframe issues emerging from recent debates, and offer different ways of thinking, talking, and embodying a culture–voice relationship in the training of actors' voices? This article critically examines the ways in which the actor's voice, and by extension the training of that voice, inform the material conditions for the production of meaning in contemporary devised performance and suggests one practical way voice trainers can create a praxis that places voice and culture at the centre of the training and theatre-making process. Using the author's voice classes at the Korean National University of Arts (KNUA) in Seoul, Korea, as case studies, this article investigates issues through a practice-as-research methodological approach within a series of performance projects called the Namdaemun Market Projects.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".