Musicality and Intrafamily Translation: With Reference to European Languages and Chinese
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".