A Creditable Performance under the Circumstances? Suematsu Kenchô and the Pre-Waley Tale of Genji
Bibliographic record
Abstract
Before Suematsu’s 1882 translation of theTale of Genji, the information available in the West about Murasaki Shikibu’s masterpiece was sketchy and erroneous. The main objectives of this translator were to improve Japan’s political status by demonstrating that it has a rich literary tradition, and to make known to Westerners what is in effect that nation’s “cultural scripture” (Rowley). Reaction to his version was conflicted: readers and reviewers are curious about the previously unsuspected literary wealth presented to them, but struggle to comprehend and find points of reference. My article focuses on the circumstances that made possible this early representation of Japanese literature, while paradoxically keeping theGenjifrom being widely read and admired until Waley’s famous translation appeared some 40 years later. I argue that Suematsu, in using this book to critique Anglo-American imperialism, nonetheless reveals his own ambivalent relationship with the text and its author. Further, Western audiences were ill-equipped to judge what they were reading, as well as reluctant to accept a non-European interpreter, and thus the reception of this world masterpiece was long stalled for reasons that had little to do with literary or translation quality.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".