Plurilingualism amid the panoply of lingualisms: addressing critiques and misconceptions in education
Bibliographic record
Abstract
Today, scholars and students face an array of lingualisms: bilingualism, multilingualism, polylingualism, metrolingualism plurilingualism, codeswitching, codemeshing, and translanguaging, among others. Plurilingualism can be understood as the study of individuals’ repertoires and agency in several languages, in different contexts, in which the individual is the locus and actor of contact; accordingly, a person’s languages and cultures interrelate and change over time, depending on individual biographies, social trajectories, and life paths. The term ‘plurilingual competence’ adds emphasis on learners’ agency, and constraints and opportunities in educational contexts. We discuss where and how plurilingualism fits among the other lingualisms, its similarities and differences, with an example of plurilingual pedagogy and practice from a university in Vancouver, Canada. In doing so, we challenge three common critiques of/misconceptions about plurilingualism: (i) that it is based on an invalid static binary between the social and the individual, (ii) that it is over-agentive, and (iii) that it can serve to reinforce social inequities within a neoliberal world order.
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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 | 0.043 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.031 | 0.181 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".