“We are all (potential) plurilinguals”: Plurilingualism as an Overarching, Holistic Concept
Bibliographic record
Abstract
In spite of the spark that plurilingualism has given throughout Europe and beyond to the idea that linguistic and cultural diversity is an asset rather than an obstacle, the term plurilingualism itself has not frequently been used in the English-speaking world. Beginning with an analysis of this issue, this paper aims to help readers better understand the nature of the concept of plurilingualism and reflect on its social and educational value. To do so, it firstly presents the term from a historical and comparative perspective in relation to other terms used in the English-speaking literature. It then moves on to explain the crucial difference between plurilingualism and multilingualism, thus introducing the notion of dynamic repertoire and its underlying theoretical perspective. Finally, the article introduces the descriptors for plurilingual and pluricultural competence from the newly released CEFR Companion Volume, together with the potential for these descriptors to facilitate mediation and plurilanguaging among learners and to foster a new, open, and positive attitude towards linguistic and cultural diversity in language classes. Keywords: plurilingualism, multilingualism, plurilanguaging, CEFR, mediation
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".