Translating Japanese Onomatopoeia into Finnish in Literature: A Case Study
Bibliographic record
Abstract
Japanese is a language rich in onomatopoeic and mimetic words, words that mimic sounds and other phenomena with their form. They are an integral part of the language and are used in nearly all situations, they also pose their own peculiar challenge to both learners and translators of Japanese. This study examines the Japanese onomatopoeic and mimetic words in the novel Sensei no kaban by Hiromi Kawakami, and their translations in its Finnish translation, to determine what techniques are most commonly used and why? As Finnish is also said to have a rich onomatopoeic and mimetic vocabulary, the frequency at which these terms are translated into equivalent onomatopoeic or mimetic words is also examined. The results show that the majority of the Japanese onomatopoeic and mimetic words, most of which function as adverbs, are translated as adverbs or verbs or they are completely omitted. Exactly a quarter of the examined cases have been translated using onomatopoeic or mimetic words, most of which are verbs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".