The Translation of Culture-Bound Elements into Finnish in the Post-War Period
Bibliographic record
Abstract
Culture-bound elements, such as proper names and food items, not only place the story of a book in a specific culture and period of time, but also imply certain values and create an ambience. These elements also have an effect on how the reader identifies with the story and characters. Thus, it is important to find the most appropriate strategy to translate such elements. This paper considers the Finnish translation (1949) of Kenneth Grahame’s The Wind in the Willows (1908), a multi-layered and allusive children’s book set in Edwardian England, and some other children’s tales translated into Finnish around the same era. The translation of The Wind in the Willows dates back to a period of time when British culture was not yet well known in Finland. The paper argues that certain inconsistencies in the translation of culture-bound elements in the book make it difficult for target text readers to understand its layers of meaning and to identify with the characters. Similar inconsistencies in the translation of culture-bound elements are found in other Finnish translations of children’s books from the same period. The findings may be explained by a limited knowledge of foreign cultures in post-war Finland.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".