Bibliographic record
Abstract
This study, based on two questionnaires directed to translation lecturers in UK Higher Education (HE), aims to explore the teacher awareness of learner autonomy in the UK university translation classroom, and the extent to which students of translation are encouraged to become autonomous learners. It covers six aspects of translation education, i.e., objective setting, learning strategies, resources, technology, learner reflection and assessment, and teacher’s role. The results provide insights to the teachers’ understanding of student choice, control and responsibility in autonomous translation learning. The findings suggest that the translation students in UK HE are offered the most choice in translation resources and technologies, and the least choice in co-deciding translation syllabi, specialized professional goal and teaching materials; they seem to share consistent control with their teachers over the whole learning process, from goal-setting, to translation task completion, group collaboration, final version and translation quality criteria decision, to self-evaluation and reflection; they are encouraged to take responsibility for their translation products, collaboration in class, for learning to translate, and learning to evaluate and reflect. Nevertheless, the importance of encouraging student self-evaluation, peer-evaluation and self reflection in UK translation education seems to be underestimated, and is recommended to be brought to the forefront of further research.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".