Sining Kambayoka Ensemble’s <i>Bayok</i> : Connecting Philippines and Canada in Teaching Voice and Performance
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".