THE OCCURRENCE AND PERCEPTION OF LISTENER VISUAL CUES DURING NONUNDERSTANDING EPISODES
Bibliographic record
Abstract
Abstract This research report examines the occurrence of listener visual cues during nonunderstanding episodes and investigates raters’ sensitivity to those cues. Nonunderstanding episodes (n = 21) and length-matched understanding episodes (n = 21) were taken from a larger dataset of video-recorded conversations between second language (L2) English speakers and a bilingual French-English interlocutor (McDonough, Trofimovich, Dao, & Abashidze, 2018). Episode videos were analyzed for the occurrence of listener visual cues, such as head nods, blinks, facial expressions, and holds. Videos of the listener’s face were manipulated to create three rating conditions: clear voice/clear face, distorted voice/clear face, and clear voice/blurred face. Raters in the same speech community (N = 66) were assigned to a video condition to assess the listener’s comprehension. Results revealed differences in the occurrence of listener visual cues between the understanding and nonunderstanding episodes. In addition, raters gave lower ratings of listener comprehension when they had access to the listener’s visual cues.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".