Burning Down the Little House on the Prairie: Asian Pioneers in Contemporary North America
Bibliographic record
Abstract
espanolThe Kappa Child (2001), de la autora canadiense de origen japones Hiromi Goto y Stealing Buddah's Dinner (2007), de la autora de origen vietnamita Bich Minh Nguyen, ofrecen narrativas de infancia de ninas asiaticas que crecen en Norteamerica en las decadas pre-multiculturales de los anos 70 y 80, cuando la palabra etnico no estaba aun de moda y la asimilacion a la cultura blanca dominante era el mayor deseo de cualquier nina. En ambos textos literarios, las narradoras estan fascinadas por la historia de continuo desplazamiento y reemplazamiento de La casa de la pradera, de la autora estadounidense Laura Ingalls Wilder (1935), y ambas se sumen en la misma desilusion debido a la distancia racial y etnica que hace imposible que ellas se conviertan en autenticas Lauras en sus respectivos contextos. Este articulo intenta evaluar la influencia de esta narrativa clasica de migracion pionera (interna) en la percepcion de su racializacion por parte de dos inmigrantes asiaticas en Norteamerica, su propia valoracion critica del racismo en los textos de Ingalls Wilder y el consecuente proceso de construccion de las subjetividades racializadas de las narradoras de Goto y Nguyen. EnglishThe Kappa Child (2001) by Japanese Canadian author Hiromi Goto and Stealing Buddah's Dinner (2007) by Vietnamese American Bich Minh Nguyen portray narratives of Asian girls growing up in North America in the pre-multiculturalism decades of the 70s and 80s, when ethnic was not a fashionable term and assimilation into mainstream white culture was any girl's most wanted desire. In both literary texts, the girl narrators are fascinated by American author Laura Ingalls Wilder's narrative of continuous displacement and re-settlement, Little House on the Prairie (1935), and they both become equally disillusioned by the racial and ethnic gaps that make it impossible for them to become true Laura Ingalls in their respective environments. This article attempts to assess the influence of this classic pioneer narrative of (internal) migration on the perception of racialization of these two Asian migrants to North America, on their own critical evaluation of the racism in Ingalls Wilder's texts and on the consequent process of construction of racialized subjectivities by Goto's and Nguyen's narrators.
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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 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".