The flexible semantic integration of gestures and words
Bibliographic record
Abstract
One measure of the communicative function of gestures is to test how speakers’ gestures are influenced by whether an addressee can see them or not, that is, by manipulating visibility between participants. We question traditional dependent variables (i.e., rate measures), suggesting that they may have been insufficient for capturing essential differences in the gestures speakers use in each condition. We propose that investigating the qualitative features of gestures is a more nuanced, and ultimately more informative approach. We examined how speakers distributed information between their gestures and words, testing whether this distribution was affected by the visibility of their addressee. Twenty pairs of undergraduates took part in conversations that were either face to face (n = 10) or on the telephone (n = 10). Each speaker described a drawing of an elaborate dress to the addressee. We used a semantic feature analysis to analyze descriptions of the dress’ skirt and assessed when words or gestures contributed information about five categories pertaining to features of the skirt’s unusual shape. Although speakers’ rates of gesturing and number of words did not vary significantly between conditions, speakers contributed more information and conveyed more categories in their gestures when the addressee would see them, while words carried the informational burden when addressees would not see the gestures (p’s < .001). These results suggest that gestures serve a communicative function. The semantic feature analysis is thus an example of how to explore gestures’ qualitative features within a quantitative paradigm.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".