Bibliographic record
Abstract
During the years of Japan's “bubble” economy, writers and artists in the United States became increasingly susceptible to “Japan-bashing,” a discourse that objectified Japanese for their trade practices, overseas purchases, and tourist presence. In the following article, I draw upon a range of cultural texts, from Truman Capote's novella Breakfast at Tiffany's to Michael Crichton's novel Rising Sun , in order to investigate how the trope of the camera-toting Japanese expatriate encapsulated the fears of the era. I then move to explore the ways in which Japanese Americans negotiated these tropes in their writings, paying particular attention to Ruth Ozeki's novel My Year of Meats . I hypothesize that Japanese Americans remained aware of the phenomenon of “Japan-bashing” throughout the era, yet did not confront it in a sustained fashion. Instead, tropes were either dismissed out of hand or, as in Ozeki's case, incorporated into a narrative before undergoing a process of gradual dismantlement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".