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Record W2064289727 · doi:10.7202/012995ar

Musicality and Intrafamily Translation: With Reference to European Languages and Chinese

2006· article· en· W2064289727 on OpenAlexvenueno aff
Laurence Wong

Bibliographic record

VenueMeta Journal des traducteurs · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMusicalityLinguisticsMeaning (existential)Denotation (semiotics)GermanComputer sciencePsychologySemioticsLiteraturePhilosophyArtMusical

Abstract

fetched live from OpenAlex

Most practitioners of translation agree that translation is at best an ersatz, able to get across only part of the source text’s meaning, which is meaning on two levels: the semantic and the phonological. Even in translating an apparently simple lexical item, to say nothing of long stretches of discourse, they are keenly aware of what is being left out. On the semantic level, for example, the denotation of a lexical item may sometimes be preserved almost intact. However, its connotations, associations, or nuances, which can elicit subtle responses from readers of the original, often defy the process of carrying over or across , which is what transferre , the Latin word from which translate is derived, means. Yet, compared with musicality, a feature on the phonological level, all features on the semantic level will become relatively easy. With reference to translations of Dante’s Divine Comedy in Spanish, French, Latin, English, German, and Chinese, as well as translations of Shakespeare’s Macbeth in Italian, this paper discusses musicality as the most recalcitrant of all features in a source-language text, and attempts to show how, depending on factors to be examined in detail, intrafamily translation, that is, translation between languages of the same family, can capture the original music with varying degrees of success.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.388

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 head0.061
GPT teacher head0.283
Teacher spread0.223 · 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 designOther design
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
Published2006
Admission routes1
Has abstractyes

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