Leveraging Virtual Learning Environments for Training Interpreter Trainers
Bibliographic record
Abstract
While the demand for conference interpreters in traditional language combinations (the more widely used languages) is decreasing, the need for experts in less widely used languages is rapidly increasing with each enlargement of the EU. Post-war peace-keeping operations as well as warcrime tribunals have also increased the need for high-level interpreters in languages hitherto not used in the international arena and consequently more well-trained interpreter trainers both for traditional programs as well as ad-hoc intensive programs must be available. Interpreters are a highly mobile community of professionals, unable to be physically present in a university for long periods of time to be trained as trainers. TheCertificate course for Interpreter Trainers at ETI(University of Geneva) has been offering the only postgraduate course for training interpreter trainers since 1996. To meet the demand for training around the world theCertificate courseis now offered in a blended format: Nine months of distance learning are blended with one week of faceto-face learning. The portal ( www.unige.ch/eti/certificate/training ) offers a rich learning environment with a number of tools to implement the philosophy of collaborative learning. With its public access and a special section for students of interpreting the portal has become an international meeting point for interpreter trainers where participants in theCertificate courseinteract with interpreting students at ETI, and interpreter trainers from schools around the world can interact with theCertificateteaching staff and students. This paper reports on the first systematic assessment of both the learning environment and the learning outcomes of theCertificate course.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".