Translation Strategies for Culture-specific Items in the Lithuanian Versions of Four British and Canadian Novels for Young People
Bibliographic record
Abstract
This thesis analyses the translation strategies for culture-specific items (CSIs) in the translations by four different Lithuanian translators of four British and Canadian novels for young people, "Alone at Ninety Foot" (2001) by Katherine Holubitsky, "Hit and Run" (2003) by Norah McClintock, "Double Act" (1996) by Jacqueline Wilson and "The Borrowers" (1952) by Mary Norton. All these novels have a great variety of culture-specific items, often reflecting the lives of children and adolescents and issues that are important to them. The analysis of translation of culture-specific items is based on the strategies suggested by Eirlys E. Davies, while the categories of culture-specific items that are chosen for deeper discussion are those of a higher importance for the characters or themes of the novel. Statistical analysis of the strategies helps to form a clearer picture of the strategic choices preferred by each of the four Lithuanian translators.\nThe present work is divided into five sections and has two appendices. Section One introduces the purpose of the work and provides some information about the object of analysis: the translation of cultural references in four British and Canadian novels for young people. Section Two explains the terminology used for the analysis of the translation of culture-specific items. Section Three is divided into eight sub-sections: 3.1, 3.3, 3.5 and 3.7 discuss the importance of some categories of culture-specific items in each novel, while... [to full 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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".