Can language classrooms take the multilingual turn?
Bibliographic record
Abstract
For the past three decades, momentum has gathered in favour of a multilingual turn in second language acquisition research and teaching. Multicompetence has been proposed to replace nativeness and monolingualism to measure L2 learners’ success. This proposed shift has not made its way into L2 teaching settings. The language presented to L2 learners is a set of monolingual, standard norms often removed from actual target language practices, implying that these linguistic features are adequate for all situations. We propose that the multilingual shift has not taken place in practice because language features associated with monolingual nativeness are necessary in many of the communicative situations L2 users encounter. Adapting the concepts of communicative distance and immediacy in order to integrate multilingual communications, the purpose of this article is to demonstrate how these situations and their oral or written realisations are located on a pluridimensional conceptional continuum. We illustrate how monolingual standard norms and code-meshing practices represent the model’s most distant and immediate communicative ends, respectively. We argue that traditional mono/multilingual and native/non-native oppositions can be reframed in order to become legitimate models for L2 classrooms.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".