Representations are Misrepresentations: The Case of Cover Designs of Banana Yoshimoto’s Kitchen
Bibliographic record
Abstract
Megan Backus’s English translation of Kitchen is notable for being both a critical and commercial success. Critics have praised its readability but noted that its translation strategy is foreignizing. Although its cover designs have not been discussed in relation to domestication and foreignization, the strategies are worthy of mention, because they have huge implications for the symbolic representation of marginalized cultures and dominant counterparts. The American publisher Grove Press uses a foreignizing strategy which succeeds in producing an intelligible image of the Oriental novel which at the same time appeals to readers, and the design accelerated the popularity of this novella in the US. In contrast, the first UK edition uses a photograph of a weeping geisha, even though the main character is a young Americanized Japanese woman. Most importantly, there are no geishas anywhere in this story. The cover may seem foreignizing at first glance, but is in fact, obviously domesticating. This design fits the stereotyped mould of what Westerners regard as “a typical Japanese image,” completely unrelated to the content. This paper investigates how Kitchen has been represented in Western countries, with a focus on the cover designs of the translations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".