Learner Code-Switching in the Content-Based Foreign Language Classroom
Bibliographic record
Abstract
This article is republished from The Canadian Modern Language Review,60, 4, pp. 501–526. It is published as an article exchange between the MLJ and the CMLR. The articles for the exchange were selected by committees from the Editorial Board of each journal according to the following criteria: articles of particular relevance to international readers, especially those in the United States and Canada; and articles that are likely to provoke scholarly discussion among readers of the journal of their republication. The MLJ thanks Keiko Koda, chair, Michael Everson, Lourdes Ortega, and Ross Steele for their work selecting this CMLR article for republication in the MLJ. The MLJ article to appear in the CMLR, 61, 5, is: “Second Language Acquisition as Situated Practice: Task Accomplishment in the French Second Language Classroom,” by Lorenza Mondada and Simona Pekarek Doehler (MLJ, 88, 2004, pp. 501–518). The Editors of both journals hope their readers will find this sharing of scholarship interesting and beneficial. Using a framework based on conversation analysis (Auer, 1984, 1995, 1998), this article presents an analysis of learner code-switching between first language (L1) and second language (L2) in an advanced foreign language (FL) classroom. It was found that students code-switch not only as a fallback method when their knowledge of the L2 fails them, or for other participant-related functions, but also for discourse-related functions that contexualize the interactional meaning of their utterances. These uses strikingly resemble code-switching patterns in non-classroom bilingual settings and show that language learners are able to conceptualize the classroom as a bilingual space. Learners orient to the classroom as a community of practice (Wenger, 1998) through their code-switching patterns as manifestations of a shared understanding about their actions and about themselves as members of that community.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".