Translation of Children’s Rhyme Bible Storybook on “The Creation” from English into Indonesian
Bibliographic record
Abstract
Foreign children’s literature has been translated in various target languages and the most translated stories are from the Bible. The uniqueness of this study is on the rhyming text translation. It is very challenging in translating rhyming text due to distinct features of orality, sociocultural context, lexical and grammatical issues. This paper aims at 1) exploring rhyme types found both in the source text and in the target text; 2) investigating the use of the translation techniques; 3) examining the translation shift; and 4) assessing the translation quality of the rhyming translated text. This is a descriptive qualitative study that applied the content analysis design. The collection of data is through focus group discussion, note-taking and quality translation (QT) rating instrument. Then data are analyzed using analyses of domain, taxonomy, and componential. The findings present 9 data of rhyme types identified in the source text and 10 data of rhyme types in the target text. Besides, there are 12 translation techniques frequently used. Furthermore, the translation shift exists in the translated text indicated by 34 data. Finally, the assessment of translation quality reveals the rates of three aspects of the parameter namely accuracy (2.22), acceptability (3), and readability (3) in which its overall score is 2.61. The overall score of the translation quality reveals that the translation meaningfully succeeds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".