2B or Not 2B Plurilingual? Navigating Languages Literacies, and Plurilingual Competence in Postsecondary Education in Canada
Bibliographic record
Abstract
In this article, the researchers employ the framework of plurilingualism and plurilingual competence in a field that has traditionally been dominated by reified conceptualizations of multilingualism that view bi/multilingualism as balanced and complete competence in discrete codes. They present data from a qualitative, longitudinal study of the interplays between the social, cultural, and linguistic in the multiple languages and literacy practices of transnational students at a university in Vancouver, Canada. Their findings question the role of academic English as the sole conduit to success for participants in higher education. They suggest that this relates back to how plurilingualism is defined and integrates the key idea that learning skills, multilingual literacies, (inter)cultural experiences, and different forms of knowledge are transferable and thus constitute assets and tools for better learning (Castellotti & Moore, 2010; Coste, Moore, & Zarate, 1997). Participants revealed a considerable degree of fluidity in their languages and literacy practices as well as shifts in perceptions and practice that change according to context. They proved to be highly plurilingual, reflexively and knowledgeably (Giddens, 1984) moving from contexts in which they mixed different languages and scripts freely to contexts in which they adhered to more normative senses of discrete monolingual practices in English and community languages.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".