The interplay between metalanguage, feedback, and meaning negotiation in oral interaction
Bibliographic record
Abstract
The present article explores the affordances virtual exchanges provide to foster a focus on form, interactional feedback, and meaning negotiation in language related episodes (LREs) occurring in interaction between learners of English and learners of Spanish as a foreign language. The participants, 36 students enrolled in language courses at two universities in two different countries, took part in a virtual exchange which involved carrying out three 40-minute video calls in pairs. These calls were video recorded and constituted the data from which different types of LREs were extracted. The recordings from the first and the last video calls, which took place two and a half months apart, were transcribed and analyzed. Data analyses revealed that learners gave significantly more feedback during the last interactive task, and that only in the case of LREs initiated by L2 speakers did this lead to more repairs and a higher resolution rate of the episodes. The data also showed that the presence of metalinguistic information led to an increased number of repairs, and that reactive LREs initiated by L1 speakers and preemptive LREs initiated by L2 speakers displayed different rates of interactional feedback, meaning negotiation, modified output, and repairs.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".