Gesture frequency is linked to story-telling style: evidence from bilinguals
Bibliographic record
Abstract
abstract Individuals differ in how frequently they gesture. It is not clear whether gesture frequency is related to culture, since varied results have been reported. The purpose of this study was to test whether the frequency of representational gestures is linked with story-telling style. Previous research showed individual and cross-cultural differences in story-telling style, some preferring to tell a chronicle (how it happened) or an evaluative story (why it happened). We hypothesized that high gesture frequency might be strongly associated with using a chronicle style, since both rely on visuospatial imagery. Four groups of bilinguals, English as their second language (L2) participated. Their first language (L1) was one of: Mandarin, Hindi, French, or Spanish. Participants watched a cartoon and told the story, once in English, once in L1. The results showed group differences in the rate of gesture use: the Chinese and Hindi L1 participants gestured less frequently than the French and Spanish L1 participants. The participants from Asian cultures were more likely to tell an evaluative story and the Romance-language L1 participants a chronicle. We conclude that these culture/language groups differ in story-telling style. A chronicle style is associated with more gesture production than an evaluative style.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".