Collaborative Writing in a Third Language: How Writers Use and View Their Plurilingual Repertoire During Collaborative Writing Tasks
Bibliographic record
Abstract
Recent years have witnessed major development in plurilingual pedagogies which support the use of learners’ repertoire of languages in language learning contexts (Payant & Galante, 2022; Piccardo, 2013). However, little research has been undertaken to examine adult plurilingual learners’ perceptions towards the use of their languages during authentic collaborative writing tasks and contrasted these views with their actual behaviours. In this case study, six plurilingual adult learners of English in a Canadian university with three unique L1s (Romanian, Russian, Spanish) completed two collaborative writing tasks on two separate occasions. Each dyad shared the same linguistic profiles and were encouraged to draw on their entire repertoire to complete the tasks. Semi-structured interview data shows differing levels of openness towards L1 and L2 (French) use during language-learning writing tasks. The analysis of the interaction confirms multiple uses for the L1; however, the L2 was seldom observed during interactions. The findings are discussed from a plurilingual lens and pedagogical implications are discussed.
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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 | 0.006 | 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 | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".