Examining the Effectiveness of Simulated Interpreting Projects: Students’ Perspectives
Bibliographic record
Abstract
In recent decades, bridging the gap between university-based interpreting teaching and industry needs has been increasingly important as an emerging area of interpreter education and training. Many interpreter educators and practitioners have introduced authentic interpreting practices (e.g. mock conference, role-play, conference observation, field training) into their classrooms and received positive feedback from student interpreters. This study investigates the use of simulated interpreting projects, which have been designed by the interpreter trainer to make student interpreters’ learning more stimulating and effective. Findings from this empirical study have demonstrated that the simulated interpreting projects not only enhanced student interpreters’ skills for and knowledge about the interpreting profession, but also cultivated their vocational skills and investigation capabilities which are crucial for many other professions. This research contributes to the current understanding of enhancing student interpreters’ learning experience via real-life activities. It introduces a fresh perspective for designing an investigative learning model for student interpreters. It also has practical implications for interpreting pedagogy, offering theoretical and empirical support for the changing attitudes and approaches in interpreter education and training.
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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".