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Record W2161912411 · doi:10.7202/1018809ar

Representations are Misrepresentations: The Case of Cover Designs of Banana Yoshimoto’s Kitchen

2013· article· en· W2161912411 on OpenAlexvenueno aff
Hiroko Furukawa

Bibliographic record

VenueTTR traduction terminologie rédaction · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCharacter (mathematics)PopularityRepresentation (politics)NovellaCover (algebra)HistoryAestheticsRelation (database)SociologyMedia studiesLiteratureArtLawComputer sciencePolitical scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.192
GPT teacher head0.339
Teacher spread0.147 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2013
Admission routes1
Has abstractyes

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