More Than Words: An Integrated Framework for Exploring Gestures’ Role in Bilinguals’ Use of Two Languages for Making Mathematical Meaning
Bibliographic record
Abstract
Abstract Gestures play a role in perception, production, and comprehension of language and have been shown to differ cross-linguistically and cross-culturally in aspects of performance and form-meaning relationships. Furthermore, gestures can serve as analytical tools to access tacit embodied-imagistic mathematical meanings that add to verbal-linguistic dimensions of meaning. At the same time, language plays important roles in interaction and cognition, influencing bilinguals’ learning of mathematics. Still, there is only very little research attending to the use of gestures of multilinguals as means to better understand the relationships between their language use and their mathematical thinking. This paper builds on research on multilingualism and on gestures—related and unrelated to mathematics education—to motivate and develop a framework for understanding better mathematics thinking and learning of multilinguals through integrating gesture analysis as related to languages, culture, and the use of registers. The application of this framework will be illustrated through two case studies in which we analyse interview data of a bilingual student and a bilingual mathematics teacher—focusing on gestures and language use while talking about the mathematical concept of ‘power’—or exponents—in Farsi (Persian) and in English. From analyzing the gestures’ form-meaning relations and their functions as related to hybrid language practices, we hypothesize on the vernacular and mathematical context as activated in both speech and gesture and on how it relates to mathematical meaning. From this, we draw practical implications for multilingual mathematical learning contexts and discuss implications for research on multilinguals’ mathematical thinking and learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".