Bibliographic record
Abstract
“My immigrant experience is not the same as these women, but I felt it in my bones. Because to be here is to be the same,” laments Eileen Cheng-Yin Chow, a professor of Asian American and diaspora studies at Duke University, following the Atlanta Spa killings. The sexualization of Asian women has persisted since their first arrival to the Americas in the 20th century. Not only has this caused direct violence, like the Atlanta Spa shooting in March 2021, which resulted in the death of 6 Asian women, but also the ‘orientalizing’ perception and self-perception of Asian American Women (AAW). In this paper, I explore the historical origins of this orientalization as it relates to media and then analyze contemporary art media (poetry, photography, etc.) from AAW to explore how this has affected modern self-perception. By focusing on the direct perspective of AAW, I explore different ways orientalism is synthesized through AAW identity, dissonance, fragmentation, and rebellion. Through my paper, I conclude that the intersectionality between orientalism, gender, and nationality has caused a profound disillusion from traditional and cultural expectations of the ‘homeland’ as well as disillusioned from the stereotypical expectations of the Americas. This, however, may contribute to the creation of a new identity, which hinges on the unique aspects of intersectionality, which focuses on the AAW voice instead of the colonial voice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".