Dancing on the (Bamboo) Ceiling: Performing while Asian in U.S. Postmodern Dance
Bibliographic record
Abstract
It was a typically hot, rainy Sunday in Hong Kong.I turned the corner, walked up a set of stairs to cross the busy street, and encountered an elevated flyover full of Filipinos enjoying an early dinner of adobo, barbeque, and pancit-foods typically served in fiestas and other community-gathering spaces.People were playing bingo, dancing, and singing along to karaoke.The mood felt celebratory, and folx were enjoying each other's company.After walking by several spaces with similar scenes across Wan Chai, a commercial district where many Filipinos gather from the various places where they work for expatriates and local Hong Kong households, I realized that these Filipinos were part of the large group of Overseas Filipino Workers (OFWs) that make up much of the workforce of many countries outside of the Philippines.These countries include Hong Kong, Australia, Japan, the United Arab Emirates, Canada, and the United States, among others.In 2020, there were 1.77 million OFWs living outside of the Philippines contributing to approximately 9% of the national economy through remittances. 1 Demand for domestic and healthcare workers has remained consistent in recent years, and Filipinos make up much of the care economy in many countries around the world.In the foreword to Grace Chang's Disposable Domestics: Immigrant Women Workers in the Global Economy, Ai-jen Poo writes, "Domestic work-the work of caring for children, 1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".