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Record W1575469226 · doi:10.7202/1013399ar

Problems in Translating Culture: The Translated Titles of Fusheng Liuji1

2013· article· en· W1575469226 on OpenAlexvenueno aff
Charles Kwong

Bibliographic record

VenueTTR traduction terminologie rédaction · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsNoticeMediationLinguisticsTarget cultureReading (process)Focus (optics)PerceptionExpression (computer science)SociologyEpistemologyComputer sciencePhilosophyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Translating culture poses fundamental problems of perception and conception far deeper than matters of linguistic expression. This essay explores some of these problems by examining Fusheng liuji ( Six Records of a Floating Life ), a Chinese autobiographical text that has been translated into fourteen Asian and European languages. Even without going into the details of the rendered versions, one can notice various forms of intercultural mediation and reshaping in the translated titles and added subtitles. At one end is direct, partly helpless substitution: lexically flawless “float” cannot encompass the rich matrix of philosophical connotations and artistic resonances of fu in the source culture. At the other end is active reshaping: recasting, addition and omission based on interpretive (mis)reading, including a reduction of imagistic language into abstract concept (e.g., fu becomes “fleeting”). Through examining 17 renditions of the title of Fusheng liuji , this essay offers a case study that helps to cast light on the unavoidable factor of intercultural mediation in the translation process, with special focus on the translation of philosophical and aesthetic concepts. Some forms of mediation carry more significant effects than others, and there may be differences in verbal resources and orientations in various languages worthy of notice.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.987

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.280
Teacher spread0.182 · 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 designNot applicable
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

Citations6
Published2013
Admission routes1
Has abstractyes

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