Plurilingual Chinese learners of French Lx: agentic assembling of semiotic resources for learning
Bibliographic record
Abstract
Abstract This paper reports an interview study with Chinese international students in an Anglophone university in Quebec, Canada, exploring their use of language and cross-language learning strategies to support their learning of French Lx (third language and beyond). Drawing on plurilingualism, Dynamic Model of Multilingualism, and language learning strategies, this article examines how Chinese learners made dynamic, creative, and, at times, unexpected links among Chinese and other additional languages and mobilized previous learning and professional experience to strategically enhance their French language learning. As a logographic language, Chinese is typologically distant from Latin-based languages. The focal participants, however, generated multilingual, multidirectional, and multimodal connections among the languages they knew. Their agentic assemblage of communicative repertoires for language learning contests the abyssal thinking behind the deficit-oriented label of “allophones” (those whose mother tongue is neither French nor English) that is used widely in the country. The study urges teachers and researchers to rethink language pedagogies that respond to and take full advantage of these student-directed strategies for better learning. Particularly, the paper argues for greater attention to students of non-alphabetic language backgrounds to recognize and co-learn with them about these self-initiated plurilingual strategies in order to build on their metalinguistic resources and create equitable classroom spaces for more effective teaching and learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".