Bibliographic record
Abstract
Acceptance theory considers that text is open and indefinite and different readers have different comprehension and interpretation. This essay tries to investigate the process of literature translation and analyses the causes and necessity of the retranslation of literature works from the aspect of both translators and readers. In the meantime, it looks into the criticism of traditional translation theory and the instructive importance of specific translation practice. Key words: acceptance theory, literature translation, retranslation Resume： La theorie d'acceptation considere que le texte est ouvert et indefini et les differents lecteurs ont la comprehension et l'interpretation differentes. Cet essai essaye d'etudier le processus de la traduction de litterature et analyse les causes et la necessite du retraduction de la literature travaux de l'aspect des traducteurs et des lecteurs. En attendant, il examine la critique de la theorie traditionnelle de traduction et l'importance instructive de la pratique en traduction specifique. Mots cles: theorie d'acceptation, traduction de literature, retraduction 摘 要: 接受理論認為文本具有開放性和未定性，不同的讀者會賦予其不同的理解和闡釋。本文嘗試運用此理論對文學翻譯過程進行考察，從譯者和讀者的角度出發，分析文學作品重譯現象產生的原因及其必要性。同時作者還進一步指出接受理論對於反思傳統的翻譯理論和批評，以及指導具體翻譯實踐的重要意義。 關鍵詞： 接受理論；文學翻譯；重譯
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 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 | 0.029 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".