The use of cross-linguistic mediation tasks in developing learners’ transversal competences
Bibliographic record
Abstract
Cross-linguistic mediation, a competence introduced by the Common European Framework of Reference for Languages (CEFR) in 2001, knows a new momentum after the recent publication of the CEFR Companion Volume (CEFR-CV), in 2020, which introduces new cando statements for the teaching and assessment of mediation. Situated within the framework of plurilingual and intercultural education, mediation is intended to directly contribute to the development not only of plurilingual and pluricultural competence but also of transversal competences which have recently come to the forefront in language education. This article discusses the role of cross-linguistic mediation tasks in developing learners’ transversal competences, including digital competences, intercultural understanding, organizational skills, global citizenship, social skills, media and information literacy, teamwork, and collaboration skills. This contribution aims at highlighting the link between mediation and transversal competences through the presentation and discussion of specific task examples, drawing upon the METLA project.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".