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Record W2738056040 · doi:10.5539/ass.v13n8p146

Study and Translation of Sun Tzu’s Art of War in Recent Years

2017· article· en· W2738056040 on OpenAlexvenueno aff
Jingna Qiu, Jingyuan Zhang

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
FundersProgram for New Century Excellent Talents in UniversityMinistry of Education of the People's Republic of China
KeywordsContext (archaeology)Translation studiesLinguisticsTranslation (biology)SociologyHistoryPhilosophyChemistry

Abstract

fetched live from OpenAlex

Increasing more studies and translations of Sun Tzu’s Art of War which is one of the leading military masterpieces around the world has been witnessed in recent years. This paper reviews the translation by Sawyer, Minford, Lin and Huang to probe into the characteristics and mistranslations within the framework of Context Theory in Systemic Functional Linguistics. It is found that Sawyer’s translation excels in the recontextualization of context of culture with rich para-texts, Minford tends to interpret with western discourse, Lin excels in comparatively accurate understanding of the source text and Huang’s translation merits in the recontextualization of language and military 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 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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.099
GPT teacher head0.345
Teacher spread0.245 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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