A Visual Asian American Diaspora: Belle Yang’s Hannah is My Name (2004) and Guene Luen Yang’s American Born Chinese (2006)
Bibliographic record
Abstract
The current study aims at theorizing the question of identity within the framework of postcolonial studies in two visual narratives: Belle Yang’s Hannah is My Name (2004) and Guene Luen Yang’s American Born Chinese (2006). Asian American studies have recently interrogated identity marking a shift from ethnic nationalism to recognition of multiplicity. The study also seeks to counter Orientalist stereotypes in American literature through the analysis and examination of postcolonial Asian American Diaspora to highlight a number of questions: 1) How is the identity of the Asian immigrant’s hybrid visually constructed? 2) How can Asian American visuals be addressed in non-white children’s literature? 3) What nurtures the transnational imaginations of the authors/illustrators in question? 4) What are the ramifications of transnational perspectives on Asian American narratives? 5) What are the nature of belonging and citizenship? The questions are a vehicle to investigate the cultural and ethnic politics of Chinese American literature and to explore new forms of self-identification in American literary discourse. They also yield rich insights into how to practice multiculturalism. What draws the visual narratives in question together is their postcolonial theme of reformulated identity to unsettle dichotomies within Asian American community. Furthermore, the present study explores semiotic systems in terms of image syntax, gestural, spatial and iconic signs to examine the relation between the denotative context of the narrative text and the connotation of the visual text that creates polysemous illustrations and indefinite meaning-making.
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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 | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".