Knowledge Structure and Training of Translation Teachers: An Exploratory Study of Doctoral Programmes of Translation Studies in Hong Kong
Bibliographic record
Abstract
A rapid expansion of translation programmes at all levels the world over in recent years has heightened the demand for quality translation teachers. However, little research has been carried out to date on translation teacher training – M.Phil./Ph.D. programmes of translation studies in tertiary institutions despite a plethora of studies on general translation teaching. The present study, intended as an initial attempt to address the issue, approaches the topic from a teacher education perspective and looks critically at the knowledge structure of translation teachers. On this basis, the present article reports on a qualitative case study conducted in 2004-2007 on the curriculums and particularly the needs and experiences of the research students at the M.Phil./Ph.D. programmes of translation studies in Hong Kong. Although the study was conducted in the context of Hong Kong, the findings and implications may apply to other translation teacher training programmes the world over.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".