Diasporic Tastescapes: Intersections of Food and Identity in Asian American Literature
Bibliographic record
Abstract
Esta tesis doctoral se propone explorar las culinarias presentes en una seleccion de narrativas asiatico-americanas escritas por autores como Jhumpa Lahiri, May-lee Chai, Shoba Narayan, Leslie Li, Bich Minh Nguyen, Linda Furiya, Mei Ng, Lois-Ann Yamanaka, Patricia Chao, Shirley Geok-lin Lim, Anita Desai, Sara Chin y Andrew X. Pham. Como pondra de manifiesto este trabajo de investigacion, la intrincada red de motivos culinarios que aderezan estos textos ofrece un marco incomparable para el estudio de las historias reales e imaginarias de la comunidad asiatico-americana, una minoria etnica a menudo racializada a traves de sus habitos alimenticios. Asi pues, examinare aquellos contextos literarios en los que la presencia del tropo de la comida adquiere matices simbolicos en relacion con la nostalgia del inmigrante, el sentimiento de comunidad en la diaspora, los conflictos entre generaciones o el choque cultural de llegada y retorno. Mi aproximacion al componente culinario combinara teorizaciones previas sobre el tema, tales como las de Sau-ling Cynthia Wong o Anita Mannur, a la vez que ofrecera nuevos puntos de partida para interpretar el tropo de la comida en el contexto de la globalizacion y el transnacionalismo. Considero que el analisis de estas metaforas comestibles desde estos puntos de vista resultara especialmente revelador a la hora de ahondar en los conceptos de hogar, identidad y pertenencia; todos ellos pilares de la conciencia diasporica.
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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 | 0.002 | 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 | 0.010 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".