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Record W4402140194 · doi:10.5430/wjel.v15n1p216

A Study on the Translation of Culture-Specific Items in Character Depiction in the English Version of Pu Songling’s Liaozhai Zhiyi

2024· article· en· W4402140194 on OpenAlexvenueno aff
Shuihan Yi, Ng Chwee Fang, Hazlina Abdul Halim

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsDepictionCharacter (mathematics)Translation (biology)LinguisticsLiteraturePhilosophyHistoryArtMathematicsChemistryGeometry

Abstract

fetched live from OpenAlex

The characterization through literary translation is crucial to the international dissemination and reception of literary works. Nevertheless, the research on the translation of character depiction in Liaozhai Zhiyi greatly falls behind other translation themes of Liaozhai Zhiyi. This study is intended to explore the translation of culture-specific items (CSIs) in character depiction in Liaozhai Zhiyi from Chinese to English, within the framework of character’s three dimensions: physiological, sociological and psychological. This study reveals the shifts in the characterization by investigating translator’s strategies, and the significant integration of CSIs and character depiction. The findings demonstrates that the translator’s choice of strategies influences the rendering of character depiction. It is found that the character depiction in the original text of Liaozhai Zhiyi is more mysterious, whereas in the translated text, it seems to possess a stronger sense of intimacy. This study suggests that translators thoroughly consider the culture-specific elements in character depiction, and take into account the combination of multi-strategies. It is hoped that this study contributes to the research field of character depiction translation in Liaozhai Zhiyi, and sheds light on the translation of ancient Chinese classics.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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
Published2024
Admission routes1
Has abstractyes

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