Examining translation behaviour of Turkish student translators in scientific text translation with think-aloud protocols
Bibliographic record
Abstract
The process of translation has been dramatically influenced by the latest developments in technology. Students’ behaviours during the translation process have also changed as they try to seek information and use different resources. This study aims to investigate the translation behaviour of students in an English translation department. For this purpose, 11 students were recruited. The students were asked to translate a scientific text from English into Turkish. For the analysis of their translation behaviour, Think-Aloud Protocols (TAPs) and their translated texts were used. Monologue Protocol was used to see what goes on in a prospective translator’s mind. The students were audio-recorded while translating. Then, the translations were scored and the transcriptions of the recordings were coded. The results were presented under three main themes: (i) Recruiting translation tools: when and how, (ii) Following a pattern of translation process, and (iii) Challenges: language(s), context and more. All in all, the present study highlights the importance of guiding students in the use of the appropriate tools for the translation of specialised texts, and also suggests that student translators should be more critical of Machine Translation outputs and should practice post-editing procedures in their courses.
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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 | 0.022 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".