Pantomime (Not Silent Gesture) in Multimodal Communication: Evidence From Children’s Narratives
Bibliographic record
Abstract
Pantomime has long been considered distinct from co-speech gesture. It has therefore been argued that pantomime cannot be part of gesture-speech integration. We examine pantomime as distinct from silent gesture, focusing on non-co-speech gestures that occur in the midst of children's spoken narratives. We propose that gestures with features of pantomime are an infrequent but meaningful component of a multimodal communicative strategy. We examined spontaneous non-co-speech representational gesture production in the narratives of 30 monolingual English-speaking children between the ages of 8- and 11-years. We compared the use of co-speech and non-co-speech gestures in both autobiographical and fictional narratives and examined viewpoint and the use of non-manual articulators, as well as the length of responses and narrative quality. The use of non-co-speech gestures was associated with longer narratives of equal or higher quality than those using only co-speech gestures. Non-co-speech gestures were most likely to adopt character-viewpoint and use non-manual articulators. The present study supports a deeper understanding of the term pantomime and its multimodal use by children in the integration of speech and gesture.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".