Translanguaging practices and metalinguistic reflection during negotiation of meaning in tandem virtual exchanges
Bibliographic record
Abstract
Earlier studies exploring translanguaging in virtual exchanges (Walker, 2018; Zheng et al., 2017) have mainly focused on identifying translanguaging in written chats to analyze discursive aspects and feedback processes. However, tandem virtual exchanges provide the possibility of analyzing the negotiation of meaning of linguistic aspects from the perspective of plurilingual practices, such as translanguaging, which have not yet been investigated in these contexts. The present study examines the role that the linguistic repertoires of the learners play in learner-learner interactions in tandem virtual exchanges between college-students at a Canadian and a Spanish university. Eighteen learners interacted online while carrying out oral collaborative tasks where they negotiated and co-created meaning in their respective target languages. In these interactions, the entire linguistic repertoires of the learners scaffolded the conversations and contributed to mutual understanding. Translanguaging practices occurred mostly in inquiries and explanations about linguistic aspects where metalinguistic reflection played an important role.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".