The sociocognitive functions of English use during L2 French collaborative writing tasks
Bibliographic record
Abstract
Reflecting the important role of collaborative dialogue in second language (L2) learning, collaborative writing tasks have been widely used in L2 classrooms to help students gain new knowledge and consolidate their existing knowledge about how the target language works. Although use of the first language (L1) during peer interaction has been criticized (Levine, 2003; Unamuno, 2008), collaborative dialogue research has identified how L1 English use serves several important sociocognitive functions and supports knowledge mediation in foreign language classrooms (Swain & Lapkin, 2013). This study also examines the sociocognitive functions served by English in an L2 French classroom but compares the functions used by L1 English ( n = 13) and L2 English ( n = 7) speakers during collaborative writing tasks. Their discussions during two collaborative writing tasks were transcribed, and their English use was analysed in terms of its sociocognitive function. Results showed that L1 and L2 English speakers used English for similar sociocognitive functions, mainly for generating ideas, managing the task, and discussing vocabulary. However, there were some different patterns in terms of how extensively English was used within a turn across the functions. Implications are discussed in terms of the potential benefits of using linguistic resources other than the target language in multilingual L2 classrooms.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| 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".