The Plurilingual and Multimodal Management of Participation and Subject Complexity in University CLIL Teamwork
Bibliographic record
Abstract
This paper explores the interactions of a groupwork team composed of both local and exchange students, with heterogeneous competence in English, in an English-medium CLIL context at a technical university in Catalonia. Plurilingual and multimodal conversation analysis is used to trace how the students jointly complete an academic task. The research conducted specifically analyses how students categorise themselves and each other in terms of their expertise, and the procedures and resources the students deploy to accomplish the task. The data show that participants’ heterogeneous linguistic repertoires are not an obstacle for successfully completing the task, for constructing subject knowledge, or for establishing a climate of mutual understanding and cooperation. The analysis refers to the tension emerging in the data between the interactional principles of progressivity –actions oriented towards task completion– and intersubjectivity –actions oriented towards resolving communicative difficulties. It also focuses on how co-participants mobilise diverse resources from their communicative repertoires, including plurilingual resources, gesture and material artefacts, in managing the task. The main argument put forward is that in instructional environments in which students are expected to build subject matter knowledge using languages that they are simultaneously learning (e.g. CLIL), considering their whole communicative repertoires as valuable resources for their learning is a promising approach.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 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".