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Record W2572553253 · doi:10.5539/ells.v7n1p23

On Zhang Peiji’s Prose Translation from the Perspective of the Translator’s Subjectivity

2017· article· en· W2572553253 on OpenAlexvenueno aff
Zhiwei Gu

Bibliographic record

VenueEnglish Language and Literature Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSubjectivityZhàngPerspective (graphical)LinguisticsExcellenceTranslation studiesComputer scienceStyle (visual arts)Function (biology)PoetryEpistemologyPhilosophyLiteratureArtificial intelligenceArtHistoryChina

Abstract

fetched live from OpenAlex

Chinese prose characterizes being formally-loose and essence-focused with features such as many a free style and myriads of assorted contents, among which the poetic images and artistic conceptions are so distinct that they are usually regarded as criteria for measuring its excellence. Whereas, it is the same features that impose obstacles for the prose translation, for it is difficult for the translator to grasp the whole essence of the original text in the first place and then recreate it in the translation works in another language. Therefore, during the translating process, it is inevitable not to adopt the translators’ subjectivity. And the three basic characteristics of it, namely, activeness, passiveness and purposiveness, will combine to affect the whole translating activity in one way or another. Nowadays, translation theorists both at home and abroad have conducted many studies on translators’ subjectivity. Grounded on those authoritative theories, this paper will make a tentative study on Zhang Peiji’s translation of Chinese modern prose writings. Professor Zhang’s classics Selected Modern Chinese Prose Writings will be focused. In the main body of the paper, the demonstrations of those three features of subjectivity will be elaborated with abundant examples from the two volumes of his translation works. In particular, great attention will be paid to the function of the translator’s activeness in the translation, which will be analyzed via three levels: lexical, semantic and textual. Thus, readers and translators-to-be would be better able to appreciate and draw upon those excellent translated prose.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.293
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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