Bibliographic record
Abstract
This paper intends to show how a translation competition, namely the “translation duel,” can be turned into a useful pedagogical tool to train translation students to adapt their target text to imposed discursive parameters and consequently learn the skopos theory in an intuitive, applied, and playful way. A translation duel can be defined as a translation competition between two translators (or two teams of translators) who compete against the clock to translate a source text under the constraint of imposed discursive parameters. The target text of both translators is projected on large screens to let spectators see the translations typed in real time including idea changes, correction of spelling mistakes, last-minute editing, etc. Finally, at the end of the round, the target texts are read out loud and the spectators can vote for their favorite target text. The concept of translation duel is largely inspired by the “lucha libro,” which is a creative writing competition in which writers are invited to produce a creative text in a very short time. This paper guides the reader through the implementation of a real translation duel that took place during the COVID-19 pandemic between translation students from the University of Mons (Belgium) and translation students from the Université Laval (Canada). Most importantly, this article argues that this type of activity provides four main advantages: first, a translation duel provides an intuitive introduction to the skopos theory. Secondly, it enables students to develop the natural skills on which a professional translator usually relies, such as rapidity, creativity, composure, team spirit, and interpersonal competence. Thirdly, it can take place either on-site, remotely, or in hybrid mode, with translators competing (and spectators watching) from different parts of the world. Finally, the translation duel can be seen a gamified activity that allows to enhance learning.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".