Asian Women Kick Ass: A Study of Gender Issues within Canadian Kumi-Daiko
Bibliographic record
Abstract
The pendulum has shifted for Japanese Kumi-daiko or Wadaiko (ensemble taiko drumming commonly referred to as taiko), from a site of hyper masculine musical performance, towards a reinterpretation along feminist values for female players. Japanese taiko (literally meaning ‘big drum’) has evolved from a male-dominated and -defined forum into a femaledominated performance art within North America. The emergence of kumi-daiko in Japan was primarily associated with masculine performances crystallized in images of lean muscular men in fundoshi (loincloth) furiously drumming on large taiko drums. Gender issues in kumi-daiko have been acknowledged and discussed among members of the kumi-daiko scene in Canada and America (Tusler 2003). Mark Tusler The development of this high density of female participants in Vancouver’s kumi-daiko will be discussed in a case study format, examining Canada’s first kumi-daiko ensemble, Katari Taiko, and the emergence of feminist stylized kumi-daiko. Focusing on the formative days of Katari Taiko is instrumental in highlighting the framework that has continued to foster the large numbers of women engaged in kumidaiko within the local area, if not the greater area of western Canada. Early Katari Taiko members played an important role in disseminating kumi-daiko throughout Canada via their performances and presentation of taiko workshops.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.047 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".