Bibliographic record
Abstract
Given the vibrant Filipinx Canadian community in British Columbia, there have been several efforts made and ongoing attempts to create artistic and performance spaces for them to showcase their migrant narratives. Highlighting theatre as a space for Filipinx migrant communities to collectively share their stories, customs, traditions, and culture on stage, this essay aims to offer a perspective on what Nina Lee Aquino calls the “museumization” of diasporic art. Complicating the ways in which Filipinx cultural knowledge and tradition can be distilled as artifact in theatre productions in the process of museumization, this paper examines the ways Filipinx migration in Vancouver has been staged in ways that are tailored for a dominant white foreign audience. I turn to the devised community-based theatrical production buto/buto: bones are seeds (2022) to show the importance of transformative efforts that work against museumization. This paper examines selected fragments of the production and its process that demonstrate and employ alternative ways of staging migrant narratives, enabling a reparative reimagining that confronts the burden of representation and critiques existing societal imaginaries imposed on the Filipinx immigrant body. Ultimately, this essay addresses the challenges of representational politics, recognizing that not only what stories and whose stories are told in theatre are at stake, but also how and why they are told.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".