Verbal and nonverbal disagreement in an ELF academic discussion task
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
Recent English as a Lingua Franca (ELF) studies have examined the linguistic features of disagreements during interactive academic tasks and casual conversations. Fewer studies, however, have explored nonverbal cues of disagreement, and even less is known about how interlocutors perceive disagreements. Therefore, using data from a corpus of ELF interaction, this study examined the verbal features and visual cues used by ELF university students to disagree during an academic discussion task. The disagreement episodes were selected through a content analysis of stimulated recall protocols in which a speaker stated that a disagreement had occurred. Transcripts were analyzed to classify the speaker's verbal strategies as being mitigated or unmitigated. Video recordings were examined for facial expressions, body movements, and hand gestures. Findings revealed that ELF students used mitigated linguistic strategies, such as hedges, during disagreement while gaze aversion, smiling, and head nods were the most frequent nonverbal cues. The stimulated recall data showed that disagreements were perceived as an opportunity to listen, think, and share different opinions. Implications are discussed in terms of how to interpret features of disagreement in language classrooms.
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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 | 0.000 | 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) | 0.001 | 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