Literal Translation from English and Malay in the Written Communication among Malay Learners of French
Bibliographic record
Abstract
This study intended to examine the use of literal translation from English and Malay language in the written communication. The objectives were to investigate the most present language used in the translation to French, the use of the literal translation (LT) of Malay and English in the written communication among across gender, and to determine at which sentence level (words, phrase or syntax) the translation was used by the learners. The research utilized qualitative and quantitative methods of data analysis. The study was conducted among Malay non-native speakers of French as a foreign language at Universiti Putra Malaysia. A total of 50 subjects took part in this study. The task was to complete a writing task of 150-200 words after 100 hours of French learning. The results indicated that Malay language played a more important part in the translation, where 163 elements of translation were found as opposed to 76 elements from English language. Among the translations produced by the learners, 57 items were in the word form, 77 items in the phrase form and 105 items in the sentence form. The results of this study could help in the teaching of French to Malay learners by making them aware of literal translation which already in their repertoire and by encouraging them to use the translation effectively.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".