Bibliographic record
Abstract
Studying and researching music in Asian America requires simultaneous attention to people, process, sound, and place. The term “Asian American music” is embraced by some and rejected by others because of the limitations of the term and the questions that can arise from such labels. How can a term be utilized and understood if it speaks at different times and instances to the people making the music, the place where music making occurs, and the musical sounds invoked, and yet other times because of the processes involved in the music making? (See Deborah Wong, Speak It Louder: Asian Americans Making Music [London: Profile, 2004] and Joseph S. C. Lam, “Embracing ‘Asian American Music’ as an Heuristic Device,” Journal of Asian American Studies 2, no. 1 [1999]: 29–50.) The first four sections (Theorizing Music and Asian America, Edited Volumes, Reference Works and Online Resources) are presented as one proposed entry point into the scholarship on music of and in Asian America and include a variety of content, theoretical approaches, and questions. The remaining sections are presented as merely one possible categorization of sources through the paths of landmarks, sounds, and communities. Landmarks in Asian America includes three examples of critical issues facing Asian American communities such as the incarceration of Japanese Americans, centrality of Chinatowns across North America as historical sites of cultural production, and diaspora as historical and contemporary sites across Asia and Asian America. Sounds of Asian America take specific musical genres into focus, and additionally, Communities of Asian America forefronts distinct ethnic groups within Asian American communities. These sections appear as such to facilitate access and inquiry but should be understood as fluid categorizations that can be moved around in endless configurations depending on one’s lens (and purpose) of inquiry. Sources are not intended to be comprehensive but representative and selected for this particular collection. Hawaiians, Pacific Islanders, and West Asians are historically marginalized or rendered invisible in many comprehensive works on Asian America; they each maintain distinct historical and contemporary positioning that merit independent entries and thus the current article is deliberately limited in scope with a focus on processes of East, South, and Southeast Asian Americans making music. The vast majority of sources are specific to the United States though discussions of Asian Canadian musics may also be found in numerous sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".