Literary Translation From Perspective of Reception Theory: The Case Study of Three Versions of Na Han
Bibliographic record
Abstract
This paper will focus on opinions of two major leaders of reception theory, namely, Iser’s “blanks and indeterminacy” and Jauss’s “horizon of expectations” to explain translators’ creative reproduction of literary translation. The blanks and indeterminacy in literary texts are used to reveal the style and enhance the aesthetic effects of a literary text. Reproduction of the blanks and indeterminacy in literary translation can give the target text reader space to exert their imagination and enjoy the aesthetic effects the original text expresses. This paper is going to explore three English versions from layers of rhetoric devices in Lu Xun’s Na Han, proving that the extent of reproducing the blanks and indeterminacy is determined by the horizon of expectations of different translators as well as their adjustment in merging their own horizon of expectations with that of target text readers. The paper draws a conclusion that there is not definitely good or bad translation as the translators and target text readers have different horizon of expectations in understanding literary works. Different versions give different and various blanks and indeterminacy to readers so that the target readers can give different interpretation of literary works which to some extent contribute to the artistic life of literary works.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".