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Record W4387106208 · doi:10.1080/23268263.2023.2262196

Sining Kambayoka Ensemble’s <i>Bayok</i> : Connecting Philippines and Canada in Teaching Voice and Performance

2023· article· en· W4387106208 on OpenAlexaffabout
Dennis Gupa, Pepito Sumayan, Rosa Zerrudo

Bibliographic record

VenueVoice and Speech Review · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsAppropriationSociologyIndigenousReflexivityPoliticsAttunementGenerosityAutoethnographyConversationPedagogyAestheticsGender studiesPolitical scienceEpistemologySocial scienceLawArt

Abstract

fetched live from OpenAlex

How does one mitigate the misappropriations of bayok in performance projects and voice training? Interweaving various positionalities and praxes of performance pedagogy and creation using the Meranaw bayok, this essay instigates a conversation on a decolonial and ethical appropriation of Indigenous voice style within the Philippines and Canada that seeks to connect various intercultural pedagogic and theatre praxes. By building a discourse on entwining the Philippine bayok in our theatre creation and voice training, the authors deploy a shared positionality and autoethnographic inquiry as critical approach of intersubjectivity, collaboration, and historical contextualization as interventions to misappropriation and other appropriative acts of Indigenous performance forms. In doing such, the authors enmesh three frameworks to cultivate a nascent but ongoing practice of decolonizing cultural appropriation through historical grounding, social-political contextualization, and informed collaboration. In shaping a discourse on ethical appropriation of bayok, the authors engage intercultural methods and predispositions of using bayok in teaching and performing theatrical projects while tackling certain socio-political goals like peace building, climate justice, and other global issues with self-reflexivity and collaboration.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.030
GPT teacher head0.248
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2023
Admission routes2
Has abstractyes

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