A cross-cultural study on the use of gestures: Evidence for cross-linguistic transfer?
Bibliographic record
Abstract
Anecdotal reports provide evidence of so called “hybrid” gesturer whose non-verbal behavior of one language/culture becomes visible in the other. The direction of this gestural transfer seems to occur from a high to a low frequency gesture language. The purpose of this study was therefore to test systematically 1) whether gestural transfer occurs from a high frequency gesture language to a low frequency gesture language, 2) if the frequency of production of some gesture types is more likely to be transferred than others, and 3) whether gestural transfer can also occur bi-directionally. To address these questions, we investigated the use of gestures of English–Spanish bilinguals, French–English bilinguals, and English monolinguals while retelling a cartoon. Our analysis focused on the rate of gestures and the frequency of production of gesture types. There was a significant difference in the overall rate of gestures: both bilingual groups gestured more than monolingual participants. This difference was particularly salient for iconic gestures. In addition, we found that French–English bilinguals used more deictic gestures in their L2. The results suggest that knowledge of a high frequency gesture language affects the gesture rate in a low-frequency gesture language.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".