Toward a Model of Active and Situated Learning in the Teaching of Computer-Aided Translation: Introducing the CERTT Project
Bibliographic record
Abstract
With technologies becoming more widely and firmly established in the language industries, translator education programs must produce graduates who are knowledgeable about and comfortable with today’s translation tools. How then can translator education programs meet future translators’ and employers’ needs with limited time and resources? One strategy is to adopt a more holistic approach, which seeks to integrate tool use across different elements of the program, including within “core” technology courses, in practical translation and other courses, and as part of independent study activities. Achieving this goal, however, is not without challenges, and it requires an investment of time and effort on the part of both educators and students. In 2007, a new translation technology education initiative was launched at the University of Ottawa’s School of Translation and Interpretation (UO-STI). This initiative is centered around the Collection of Electronic Resources in Translation Technologies (CERTT) project. Motivated by a desire to improve the way in which technology- related knowledge and skills are taught to translators, the CERTT team has developed a framework to assist educators and students in exploring and using a range of over thirty computer tools and resources for translators. This centralized collection of tutorials, exercises, corpora, sample files for use with tools, and other related resources is currently being integrated into LinguisTech, which is an online portal and translator’s toolbox intended to supply information and access to a range of language technologies to translation students across Canada, as well as to certain other users. This article describes some key challenges associated with teaching translation technologies and presents the underlying philosophy and framework of the CERTT project, explaining how CERTT seeks to address them. It also briefly reports on the experience of the first four years of teaching with CERTT at UO-STI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".