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Record W2500085194 · doi:10.3968/8067

On Translation Strategies of Chinese Culture-Loaded Words

2016· article· en· W2500085194 on OpenAlexvenueno aff
Chunyan Xiang

Bibliographic record

VenueCanadian social science · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsChinese cultureEquivalence (formal languages)LinguisticsDomestication and foreignizationIntercultural communicationTranslation (biology)Relevance (law)Dynamic and formal equivalenceComputer scienceReading (process)PsychologyDomesticationCommunicationChinaHistoryMachine translationPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Chinese culture-loaded words refer to the words, phrases, or idioms symbolizing unique features of Chinese culture. There are many Chinese culture-loaded words in the process of reading or translating Chinese literary works, intercultural communication. It’s accurate translation of Chinese culture-loaded words is conducive to the development of linguistic, and it is of vital importance for translation and intercultural communication. This article systematically introduces five kinds of Chinese culture-loaded words, thus summarizing foreignization and domestication translation strategy based on relevance translation theory, equivalence translation theory. Through analysis of examples, it lists different translation methods, aiming at choosing the best solution to translate different Chinese culture-loaded words. It not only enables readers to gain better understanding of Chinese literary and make intercultural communication, but also makes great contribution to diffuse Chinese culture around the world.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0040.004
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.042
GPT teacher head0.288
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2016
Admission routes1
Has abstractyes

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