MétaCan
Menu
Back to cohort
Record W4387607684 · doi:10.5430/wjel.v13n8p512

Translation Purposes Determine Everything? Appellation Translation in Northern Shaanxi Folk Songs Based on Corpus Method

2023· article· en· W4387607684 on OpenAlexvenueno aff
Yan Lin, Hazlina Abdul Halim, Farhana Muslim Mohd Jalis

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSkopos theoryMythologyTranslation (biology)Computer scienceLinguisticsNatural language processingBuddhismArtificial intelligenceLiteratureHistoryArtPhilosophyPerspective (graphical)Archaeology

Abstract

fetched live from OpenAlex

This study employs the skopos theory to analyze the translation methods used for appellations in Northern Shaanxi folk songs, a prominent genre in Chinese folk songs. The skopos theory emphasizes the importance of understanding and respecting the purpose of a translation, as it ultimately shapes the translation process and outcome. The English translations by Wang Hongyin and Wang Zhanbin are used to create a bilingual Chinese-English parallel corpus using a corpus technique. Additionally, to help the corpus software retrieve important statistics, the appellations and translation methods are manually annotated with the pertinent software. This study identifies seven primary categories of appellations, including those related to love, characters, laborers, family, mythology, allusions, and Buddhist figures. It explores the translation methods applied to each category, revealing that Wang Hongyin and Wang Zhanbin creatively adapt these appellations to convey their essence and significance to English-speaking readers while adhering to the skopos theory’s principles. This article contributes valuable insights into the translation of Northern Shaanxi folk songs, transcending cultural boundaries and enhancing cross-cultural understanding.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.291
Teacher spread0.250 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of English LanguageSame topicTranslation Studies and PracticesFrench-language works237,207