STUDENTS’ TECHNIQUES IN TRANSLATING “EUPHEMISM” FROM ENGLISH TO INDONESIAN AT STKIP INSAN MADANI AIR MOLEK
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Students in the English Department are being prepared to become future translators. Therefore, they must be capable of translating various types of texts. The source of data involved sixth-year students in the academic year 2023/2024 in STKIP Insan Madani Air Molek. The researcher selected purposively sixth-year students as because they were assumed to be taken into translation courses. This research aims to analyze the students' translation techniques related to euphemism. This research is conducted through translation assessment and the identification of techniques during the translation process. This research employs a qualitative method to understand the phenomenon being examined deeply and to provide comprehensive insights into the research. The research findings indicate that students used eight translation techniques in translating euphemism texts. These translation techniques include: each one is ranked from the most frequently used to the least used: Adaptation 13 times/12.38%, Amplification 15 times/14.28%, Calque 52 times/49.52%, Compensation 1 time/0.95%, Discursive Creation 1 time/0.95%, Generalization 6 times/5.71%, Literal Translation 14 times/13.33%, and Modulation 4 times/3.80%.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it