What Does Metalinguistic Activity in Learners' Interaction During a Collaborative L2 Writing Task Look Like?
Bibliographic record
Abstract
This article examines the metalinguistic activity that arose in the interaction of 7 groups of bilingual learners writing collaboratively in their second language (L2), English. A microanalysis of this interaction reveals that metalinguistic activity comprises 3 types of oral production: comments, speech actions, and text reformulations. Text reformulations were the most frequent type of oral production in 4 of the 7 groups, whereas in the remaining 3 groups, comments were the most frequent. Moreover, the analysis shows that comments always constituted explicit metalinguistic activity, that speech actions always comprised implicit metalinguistic activity, and that text reformulations could contain either explicit or implicit metalinguistic activity. All groups in the study exhibited implicit metalinguistic activity more frequently than explicit metalinguistic activity. Based on these findings, this article shows the importance of implicit metalinguistic activity in collaborative interaction and argues that it is a significant component of attention to language that has largely been ignored in the literature. It also argues that implicit metalinguistic activity needs to be examined further to determine the language representations underlying this activity. The article concludes by outlining a working hypothesis about the nature of these representations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".