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Record W2576153127 · doi:10.3968/9098

Translation of Temporal Dialect in Life and Death Are Wearing Me Out From the Perspectives of Scope and Background

2016· article· en· W2576153127 on OpenAlexvenueno aff
Yushan Zhao, Ya-Nan Xu

Bibliographic record

VenueStudies in literature and language · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)LinguisticsConstrual level theoryTranslation (biology)CognitionPsychologyHistoryComputer scienceSocial psychologyPhilosophyNeuroscience

Abstract

fetched live from OpenAlex

Temporal dialect is widely adopted in the novel Life and Death Are Wearing Me Out , for the novel involves many great times in China and all sorts of temporal dialects are the reveal of different eras. Therefore, in light of its importance of showing the features of different times, temporal dialect should be attached great significance to the translation studies. This paper aims to study the translation of temporal dialect in Life and Death Are Wearing Me Out from the perspectives of scope and background in Construal theory in order to verify the feasibility of scope and background in explaining the translation of temporal dialect. Based on the scope and background, encyclopedic knowledge can be formed for the purpose of understanding the temporal dialect in the original and producing the same translation version as the cognitive domains evoked in translator’s mind. What is more, suitable translation methods should be employed in the translation process under the guidance of scope and background in order to help target readers understand the theme and features of the original well.

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0040.005
Open science0.0000.002
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 head0.038
GPT teacher head0.334
Teacher spread0.296 · 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
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
Published2016
Admission routes1
Has abstractyes

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