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Record W3042366687 · doi:10.5539/ells.v10n3p49

Strategies to Represent the Hakka Culture in the Translation of Xunwu Diaocha

2020· article· en· W3042366687 on OpenAlexvenueno aff
Tong Liu

Bibliographic record

VenueEnglish Language and Literature Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPinyinLiteral translationLinguisticsRepresentation (politics)VernacularChinese cultureNothingTranslation (biology)SociologyHistoryChinese charactersSource textChinaPhilosophyPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Xunwu Diaocha (Report from Xunwu) by Mao Zedong was abundant in original material and local people’s language and characterized by the Hakka culture, including the local Hakka dialect and vernacular, social customs, foods and tools, and other aspects. This makes it difficult for non-Hakka Chinese to understand its contents, let alone English speakers who know nothing about Hakka. In attempting to make the translation smoothly understood by English speakers while not losing the Hakka flavor, American translator Roger Thompson has done a good job. By comparing Xunwu Diaocha (the original) with its English version Report from Xunwu translated by Roger R. Thompson, this paper analyzes the English expressions of the Hakka culture and discovers four translation strategies that the translator has adopted to achieve the goal of cultural representation. The strategies are Chinese Pinyin plus explanation, literal translation plus explanation, free translation plus Chinese Pinyin, and free translation plus explanation. The study reveals that through the above-mentioned strategies, the translation has well represented the Hakka culture and realizes cultural representation in its translation. Hopefully the strategies employed to represent the Hakka culture can serve as solid guidance for translations of other texts involving rich cultures.

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: none
Teacher disagreement score0.754
Threshold uncertainty score0.243

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 communication0.0000.000
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 head0.042
GPT teacher head0.300
Teacher spread0.258 · 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
Published2020
Admission routes1
Has abstractyes

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