Bibliographic record
Abstract
Contemporary globalized society is characterized by mobility and change, two phenomena that have a direct impact on the broad linguistic landscape. Language proficiency is no longer seen as a monolithic phenomenon that occurs independently of the linguistic repertoires and trajectories of learners and teachers, but rather shaped by uneven and ever‐changing competences, both linguistic and cultural. In the European context, research conducted over the past 20 years in multilingual realities of local communities and societies has brought to the forefront the notion ofplurilingualism, which is opening up new perspectives in language education. In North American academia, the paradigm shift from linguistic homogeneity and purism to heteroglossic and plurilingual competence in applied linguistics has been observed in the emergence of such concepts asdisinventing languages,translanguaging, andcode‐meshing. Starting from a historical perspective, this article examines the shared principles upon which such innovative understandings of linguistic competence are based. In particular, it investigates the specificity of plurilingualism as an individual characteristic clearly distinct from multilingualism in the light of different theoretical lenses. The author discusses the potential of such vision together with its implications. Finally, this article offers pedagogical implications for English language education in the North American context, and suggests ways to investigate the new active role that English language learners and teachers can adopt in shaping their process of learning English.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| 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".