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Record W2883129948 · doi:10.1515/sem-2016-0206

Translating iconicities of classical Chinese poetry

2018· article· en· W2883129948 on OpenAlexaff
Guangxu Zhao, Luise von Flotow

Bibliographic record

VenueSemiotica · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsIconicityPoetryPound (networking)Chinese poetryClassical Chinese poetryClassical ChineseLiteratureLinguisticsHistoryArtPhilosophyComputer science

Abstract

fetched live from OpenAlex

Abstract In the history of translating classical Chinese poetry, there are two kinds of translators. The first kind translate classical Chinese poetry “by way of intellectual, directional devices” (Yip, Wai-lim. 1969. Ezra Pound’s Cathay . Princeton, NJ: Princeton University Press: 16). What these translators are concerned with most is the coherence of their translations. They give little attention to the ideogrammic nature of Chinese characters. I call them traditional translators. These translators include those in the history of translating classical Chinese poetry from its beginning to the first decade of the twentieth century, although there are still some who translate classical Chinese poetry in this way later. The second kind of translator is highly interested in the images created by ideogrammic Chinese characters and tries to convey them in target language. We call them modernist translators. These translators are represented by some American modernist poets such as Ezra Pound, Amy Lowell, Florence Ayscough, etc. From the point of view of iconicity, modernist translators’ contribution lies in their concern with the iconic characteristics of Chinese characters. But they did not give enough attention to syntactical iconicity and textual iconicity in classical Chinese poetry.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.998

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)0.0030.000

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 head0.019
GPT teacher head0.315
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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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