“Translating” legal translation training theory into practice: the McGill University experience
Bibliographic record
Abstract
In the fall of 2020, McGill University (Montreal, Canada) launched a graduate diploma that focused on legal translation. The Graduate Diploma in Legal Translation (GDLT) aims to train the next generation of legal translators and jurilinguists in Canada. One of the main pillars of the GDLT is the formal and comprehensive interdisciplinary approach to legal translation competence development (Prieto Ramos 2011, 2015). Based on five core competences (strategic and methodological, communicative and textual, thematic and cultural, instrumental, and interpersonal and professional management), this approach is integrative and process-oriented with the aim of ensuring quality and adequacy in legal translation. McGill University used this approach as a roadmap to create the GDLT structure and plan of study. In this paper, we will offer an overview of McGill University’s initiative, providing critical analysis of the approach proposed by Prieto Ramos and of its interpretation and application for training purposes at McGill University. Our curriculum mapping methodology involves aligning the course-level learning outcomes to the broader programme-level learning outcomes which coincide with the proposed five core competences. Findings on the degree of alignment and an argument for an enriched competence training approach will be discussed in this paper.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".