The Usefulness of Different Forms of Translation in a CLIL Environment
Bibliographic record
Abstract
This article aims to explore the role translation can play within a CLIL environment. The hypothesis that the research project, on which this paper is based, wanted to prove, was that the introduction of materials and activities informed by the use of various forms of translation could lead to an enhancement of the performance in students attending CLIL courses at the level of both content and language. As Snell-Hornby stated at the end of the twentieth century, the practice of translation in our society has increasingly acquired a fundamental importance. Yet, for many years, translation has often been excluded from the language class. However, in our multicultural society, translation has become an essential tool in various professional and social contexts, including the multicultural classrooms teachers act in. This article is based on my experience as a teacher trainer (English language and CLIL methodology) and on the results obtained during research which focused, in particular, on the use of various forms of translation in CLIL courses and which extended over the school years 2017-2018 and 2018-2019. As a result, this article argues in favor of translation in the CLIL class. On the basis of the outcomes obtained, it is my contention here that Learning Units such as those presented here, where translation works in synergy with CLIL methodology, are perceived as motiving by students, thereby facilitating the development of various disciplinary and communicative skills.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".