Proper Names in the Lithuanian Translation of\nYann Martel’s <Life of Pi>
Bibliographic record
Abstract
Proper names which are rendered by various methods are found in many works of Canadian literature, \nrecently translated into Lithuanian. In this paper, I analyze one of these, Yann Martel’s . \nProper names are an important object of literary onomastics; they are important for translation \nmethodology as well. A translator should be aware of the motivation of the original text and the author’s \nidea in order to select a rendering method which reflects best the onomastic level of a piece. In fiction, it is \nnecessary to single out the names of existing places which have an established tradition in Lithuanian and \nare used in various texts (e.g., 'Kvebekas' [Quebec], 'Torontas' [Toronto]); moreover, names and family \nnames of real historical, cultural, or political personalities (e.g., 'Šekspyras' [Shakespeare], 'Bodleras' \n[Baudelaire], 'Kenedis' [Kennedy]) or real anthroponyms which are not related to a particular person \n('Džeinė' [Jane], 'ponas Raitas' [Mr. Right]) should be taken into consideration. A completely different case \noccurs with proper names created by an author. Such systems of proper names should also be reflected \nexactly in a translation. For translators of fiction texts, it is very important to grasp and properly apply the \nrules for rendering proper names in order for the original not to suffer and for a reader to easily understand \nthe sense of proper names in a text.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".