Negotiating for meaning in interaction: differences between virtual exchanges and regular online activities
Bibliographic record
Abstract
The present research explores the interactional nature of oral tasks carried out in two types of learner dyads in terms of their likelihood to foster negotiation for meaning during Language Related Episodes (LREs). Quantitative data analyses reveal how learners in same L1 dyads, Spanish English as a Foreign Language (EFL) learners, and in different L1 dyads, Canadian learners of Spanish and Spanish learners of English participating in a virtual exchange, modify their speech using negotiations and clarifications to make it comprehensible to their interlocutors. Eighteen different L1 dyads of university learners doing a virtual exchange (Canada-Spain) and eighteen dyads of Spanish-speakers learning English at the Spanish university carried out three oral communicative tasks online following the same procedures. Data were transcribed, LREs were identified, quantified for each dyad, and analyzed to determine their characteristics in terms of types of triggers, modified output, and type of feedback provided. Initial findings point to substantial differences in meaning negotiation occurring during LREs in each group. Different-L1 dyads exhibit more clarifications, meaning negotiation, and provide more feedback, which leads to higher amounts of comprehensible and modified output than learners in same L1 dyads.
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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 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".