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Record W2905402826 · doi:10.3968/10666

The Loss of Cultural Image in Literary Translation-A Case Study of the English Version of The Peony Pavilion

2018· article· en· W2905402826 on OpenAlexvenueno aff
Weifen Zhang, Yingchun Cao

Bibliographic record

VenueStudies in literature and language · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsPavilionRepetition (rhetorical device)LiteratureSymbol (formal)Expression (computer science)AestheticsLinguisticsHistoryArtComputer sciencePhilosophyArchaeology

Abstract

fetched live from OpenAlex

Cultural image is the unique cultural symbol formed in different ethnic groups due to different cultural traditions and geological environment. Cultural image is widely used in classic Chinese literary, which was a great difficulty in translation. The paper has a case study of The Peony Pavilion , where exists abundant cultural images, among which the use of image “willow” and “plum” were in high frequency. These images were elaborately designed by the author to forward the plots and contained profound connotative meanings. Therefore, the translation of these cultural images is of vital importance. This essay explores the phenomenon of the loss of cultural images in the English version translated by Wang Rongpei and Cyril Birch. There are 55 scenes in the Peony Pavilion , among which the image “plum” and “willow” have been mentioned in 32 scenes. The constant repetition of these two images helps depict vivid characters and promotes the development of plots. Unfortunately, in the translation, the expression of these images is more or less absent.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.292

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 studies0.0000.001
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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2018
Admission routes1
Has abstractyes

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