Krystyn R. Moon. Yellowface: Creating the Chinese in American Popular Music and Performance, 1850s–1920s. New Brunswick: Rutgers University Press. 2005. Pp. xi, 220. $23.95
Bibliographic record
Abstract
Similar to recent works on U.S. orientalism, Krystyn R. Moon's book argues for the centrality of popular cultural discourses on Asians and Asian Americans in shaping American culture and national identity. Moon's monograph examines how white, Chinese American, and African American songwriters, musicians, and performers have constructed and contested racial differences and hierarchies through the practice of “yellowface,” a set of racially coded modes of musical production and theatrical practices that conveyed notions of “Chineseness” as inherently foreign and inferior to American or European culture and society. While previous scholars have discussed yellowface as part of their inquiry into the formation of American orientalism, Moon is the first to provide a broad survey of yellowface performance and music in the nineteenth and early twentieth centuries. By placing the history of yellowface alongside more familiar forms of racial performance such as blackface minstrelsy, Moon points out that yellowface's emergence was part of the broader formation of a national racial vernacular on the American stage that delineated the boundaries of American identity, marking certain racial groups such as blacks as American and others such as the Chinese as perpetually foreign and incapable of national incorporation. Even as yellowface performances became more totalizing and codified with specific practices ranging from costume, makeup, orchestrated gestures, dialect, props, and musical sounds, Moon argues that yellowface constructions of racial difference and hierarchies remained unstable and ambivalent, with Chinese simultaneously taking on the polar extremes of “aversion and fascination.” She proposes viewing the history of yellowface performance in dialectical terms as a contestation between white musical composers and performers and Chinese and Chinese American performers. Even as white performers and songwriters highlighted the exoticism or cultural and physical inferiority of the Chinese in their racial performances, Chinese and Chinese American performers through their physical presence and performance on stage challenged white hegemony over the constructions of Chineseness.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.026 |
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".