Bibliographic record
Abstract
Abstract The present study examines the role that multimodality and translanguaging play in scaffolding oral interactions during language‐related episodes (LREs) involving meaning negotiation. The oral tasks carried out using synchronous video‐based computer‐mediated communication were part of a tandem virtual exchange (Spain, Canada). The participants, 18 dyads of English and Spanish college‐level learners, conducted three oral interaction tasks in pairs online. LREs were identified and transcribed and data were analyzed quantitatively and qualitatively, including all instances of translanguaging and uses of multiple modes of meaning‐making. Quantitative data revealed that translanguaging involved not only English and Spanish, but also other shared languages and occurred mostly during meaning negotiation. Additionally, the use of multimodal elements, including gestures, postures, gaze, multiple digital and physical devices (mobile devices, computers, props, notes) was examined. Qualitative data analyses revealed the interplay between multimodality and learners’ multilingual repertoires which reinforced and complemented meaning‐making during these episodes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".