Bibliographic record
Abstract
With its dynamic choreographies and booming drumbeats, taiko has gained worldwide popularity since its emergence in 1950s Japan. Harnessed by Japanese Americans in the late 1960s, taiko’s sonic largesse and buoyant energy challenged stereotypical images of Asians in America as either model minorities or sinister foreigners. While the majority of North American taiko players are Asian American, more than four hundred groups now exist across the United States and Canada, and these groups are comprised of people from a variety of racial and ethnic identities. Using ethnographic and historical approaches combined with performance description and analysis, this book explores the connections between taiko and Asian American cultural politics at the intersections of race, gender, and sexuality. Based on original and archival interviews, as well as the author’s extensive experience as a taiko player, this book highlights not only the West Coast but also the Midwest as a site for Asian American cultural production and makes embodied experience central to inquiries about identity. The book builds on insights from the fields of dance studies, ethnomusicology, performance studies, and Asian American studies to argue that taiko players from a variety of identity positions “perform Asian America” on stage, as well as in rehearsals, festivals, and schools and through interactions with audiences. While many taiko drummers play simply for the love of the form’s dynamism and physicality, this book demonstrates that politics is built into even the most mundane aspects of rehearsing and performing.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".