Bibliographic record
Abstract
Dictionaries of all types are an indispensable tool for both professional and trainee translators. However, the literature on trainee translators indicates that the skills associated with dictionary use have not been given the required attention. Knowing which dictionaries to use and how to use them efficiently when engaged in the translation process are significant aspects of translation pedagogy. In fact, facilitating the development of effective dictionary use helps develop translation competence in general. Therefore, the present article reports on a qualitative case study of successful translation students’ usage of and preferences for various types of dictionaries. The results show that successful trainee translators use dictionaries to locate synonyms or better translations for target words. Successful translation students are also reported to use dictionaries frequently to check spelling. Most respondents reported consulting the dictionary after they finished reading source texts. In line with the global move toward digitalization, the participants reported using electronic dictionaries with significantly greater frequency than paper dictionaries. In most cases, successful translation students’ use of paper dictionaries was limited to classroom examinations. The open-ended interview questions also helped to reveal the variety of dictionaries used by this group of trainee translators. Taken together, these findings have utility for translation instructors, particularly regarding the improvement of trainee translators’ experiences and the provision of assistance to less successful students.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".