The Analysis of the stage manager's utterances in Desa Kita Indonesian translation
Bibliographic record
Abstract
This thesis is about the translation of one Indonesian play entitled Desa Kita. In this play, there is one character which is considered as the most important one, the Stage Manager. Seeing the importance of the Stage Manager utterances, the writer would like to know whether the translator delivers the same message of the Source Language, and what strategy that the translator used in order to avoid the non-equivalence of the terms since there are differences in culture. Since the Stage Manager utterance?s are giving information, the writer uses a theory of translating informative text namely an informative function theory by Peter Newmark. She wants to know whether Desa Kita has a good quality of translation based on the informative function theory. There are seven strategies to avoid the nonequivalence of the meaning or terms by Mona Baker, which are also chosen by the writer for the analysis. This study is a descriptive one and she collects the data from the original book, Our Town and the Indonesian translation, Desa Kita from Act One until Act Three. The writer chooses only the Stage Manager?s utterance. The findings of the analysis reveal that the message of the Stage Manager?s utterances has already been transferred well. Although there are some nonequivalence words or terms which can be found in the translation, it does not influence the story. In addition, the strategies are used as its function that is to avoid the non-equivalence words or terms. Meanwhile the identification of the strategy of translation in the sentence of utterance which already has the same message is only for knowledge. In this study the writer includes the analysis of the other problems of non-equivalence words or terms happen outside the seven strategies, such as the identification of one-to-one translation, etc. At last, the writer concludes that the Stage Manager?s utterance has already been transferred well. Since the translator is able to reproduce the information in the Target Language text the same as what is informed in the Source Language text, the translator has done his work so well.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".