Translating modern Japanese literary prose : a theoretical approach
Bibliographic record
Abstract
This paper investigates language and translation theories as they pertain to the English translation of modern Japanese literary prose. The four chapters deal, respectively, with a general discussion of language theory; a discussion of some important theoretical issues in translation; a case study, consisting of a detailed discussion of some of the problems and issues encountered in translating a specific work of Japanese fiction; and, finally, the translation itself. Chapter I examines some influential language theories, including the concept of signification, Bakhtin's theory of heteroglossia, and Whorf's theories on how languages influence our conceptualization of reality. Language is presented as dynamic, shifting, contextual, and self-referential, expressive and at the same time creative of who we are and how we see ourselves in relation to the world around us. Chapter II examines several translation issues, including translation metaphorics, the subjectivity of the translator, the nature of fidelity in translation, translating cultural subtext and supertext, and structural differences between Japanese and English that affect translation. Translation is an interpretive art: the translated text acts as a 'meta-text' to the original, with the translator's unique, subjective interpretation intrinsic to its production. Although translation is driven by a desire for sameness, difference is the more fundamental aspect, and the translator's art lies in using these differences to illumine and complement the original. Chapter III studies the translation of a specific literary work, "Uji" (Maggot) by Fujisawa Shu. General structural problems discussed include indeterminacy and delayed determinacy of meariing, problems of tense/aspect, kanji overdetermination, and issues relating to cultural subtext and supertext. In addition, several difficult passages are analyzed to illustrate the interpretive and creative process of rendering Japanese into fluid English. Chapter IV is the translation itself, a grotesque but artfully wrought description of a maggot’s journey over the raped and murdered corpse of a young woman. The delicacy of its prose combined with the sensitive nature of its content demand that the translation be carried out with considerable tact, so as not to disturb the precarious balance between poetry and abomination that the original so successfully achieves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".