What can ‘professional vision’ tell us about teachers’ language alternation? A multimodal study of Chinese L2 classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Second-language (L2) teachers routinely switch between the target language and a shared lingua franca to secure students’ understanding and participation, yet differences between novice and expert language alternation remain under-described. Drawing on six hours of video-recorded Chinese L2 classroom interaction, this study compares 151 language alternation episodes produced by novice teachers with 40 episodes by expert teachers. Using Multimodal Conversation Analysis, the results show that both groups use language alternation proactively and retroactively. Novices alternate more often and less precisely, sometimes replacing emerging target forms; experts switch sparingly and embed English within a richer multimodal repertoire to maximize learning opportunities. Findings show that ‘professional vision’ guides teachers’ multimodal language alternation, and the resulting interactional design makes that vision visible. This study provides actionable insights for teacher educators seeking to help novices ‘learn to see’ and calibrate their use of shared languages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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