Visual cues during interaction: Are recasts different from noncorrective repetition?
Why this work is in the frame
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Bibliographic record
Abstract
Visual cues may help second language (L2) speakers perceive interactional feedback and reformulate their nontarget forms, particularly when paired with recasts, as recasts can be difficult to perceive as corrective. This study explores whether recasts have a visual signature and whether raters can perceive a recast’s corrective function. Transcripts of conversations between a bilingual French–English interlocutor and L2 English university students ( n = 24) were analysed for recasts and noncorrective repetitions with rising and declarative intonation. Videos of those excerpts ( k = 96) were then analysed for the interlocutor’s provision of visual cues during the recast and repetition turns, including eye gaze duration, nods, blinks, and other facial expressions (frowns, eyebrow raises). The videos were rated by 96 undergraduate university students who were randomly assigned to three viewing conditions: clear voice/clear face, clear voice/blurred face, or distorted voice/clear face. Using a 100-millimeter scale with two anchor points (0% = he’s making a comment, and 100% = he’s correcting an error), they rated the corrective function of the interlocutors’ responses while their eye gaze was tracked. Raters reliably distinguished recasts from repetitions through their ratings (although they were generally low), but not through their eye gaze behaviors.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 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 it