Strategies for avoiding misunderstanding in English L2 conversations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract To contribute to existing research that describes how L2 speakers avoid misunderstanding during conversation, the current study examines the strategies used by university students in Canada. The students (N = 104) were audio-recorded while carrying out two communicative tasks in pairs: exchanging personal experiences about moving to Canada, and discussing academic research studies. Transcripts were coded for the occurrence of speaker strategies (e.g. comprehension check, elaboration) and listener strategies (e.g. confirmation check, clarification request) for avoiding misunderstanding. Their use of speaker and listener strategies was compared overall and by conversational topic (i.e. personal or academic). The results indicated that the students used more speaker strategies than listener strategies and produced more speaker strategies during the academic discussion than the personal topic. Implications for L2 teaching are highlighted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it