F u z a i : Culture, Contracts & Cloud Cats In The Japanese Translation Of Kenneth Oppel’s Airborn
Bibliographic record
Abstract
In my thesis, I examine the many processes of societal, cultural and linguistic negotiation within Masaru Harada's Japanese translation of Canadian author Kenneth Oppel's 2004 young adult novel, Airborn. By approaching the notion of translation as a theoretical framework, I explore both the source text and its translation across the metatextual, textual and editorial levels, examining the tensions, commentary, and unspoken narratives on audience, culture, and the novel it self that arise from the translation process. Tracing the implications of the translation's phonetic gloss and interventional explanations, I first interrogate both the translation's assumptions about its readership, and the narrative behind the translation's integration of made-up terminology and "politeness levels" exclusive to the Japanese language into the text. I continue with an examination of the parallels between the textualization processes present in the novel's plot and the translation process, discussing the various ways in which the text itself mirrors, and even comments on issues of translation via its own narrative. Finally, I explore the narrative of expectations and assumptions surrounding the text itself with regard to Harada's own commentary about the novel's translation, editorial interventions, and the translation's ultimate commercial reception within Japan. In doing so, I foreground the multilayered nature of translation itself, and further illuminate the ways in which societal, cultural, and linguistic differences are ultimately handled and negotiated within the novel's translation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".