Bibliographic record
Abstract
Learning and teaching mathematics requires the activation of a variety of resources, which can be grouped around what I consider to be two chief sources of mathematics knowledge: the body and its activity with artifacts, and the activity with signs. The cognitive significance of the body has become one of the major topics in current psychological and cognitive studies. In particular, the ―Embodied Mind paradigm (Lakoff & Núñez, 2000) has situated the origins of all human knowledge—mathematics included—in bodily experiences and perceptions. On the other hand, knowledge formation is embedded in cultural and social contexts, and the use of signs becomes crucial in cognition. Such a polarity results in being of particular interest in the context of the teaching and learning of mathematics, a discipline traditionally considered ―abstract‖ on the one hand, and extensively based on perceivable signs on the other. Many studies in mathematics education research have framed teaching and learning activity within semiotic perspectives, focusing on written semiotic systems such as algebraic symbolism. Only recently has attention been dedicated to considering bodily means of expression as semiotic resources in the learning process, and to looking at their relationship to written mathematical symbolism (see for instance Radford, Bardini & Sabena, 2006 on the role of rhythm in the context of algebraic generalization). In this chapter, I focus on the contribution of gestures to the mathematics teaching-learning processes in the classroom context. The role of gestures will be outlined by situating them as semiotic resources and looking at how they are closely intertwined with more traditionally studied semiotic systems (such as language and written signs).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".