Bibliographic record
Abstract
The role of gestures in communication is still debated: Some claim that gestures are merely ancillary forms of expressions, whereas others suggest a central role of gestures in the development of language. In this article, I provide data in support of the overarching hypothesis that gestures have a transitional function between ergotic/epistemic movements of hands and symbolic expressions. The context for the study of these transitions is constituted by school science laboratory activities conducted by students who are also asked to describe and explain while still within proximity of the materials of their investigations. It is hypothesized that communication is distributed across the context (verbal, gestural, material) and shifts increasingly into a verbal modality as students become familiar with the phenomena they are to learn about. Furthermore, it is hypothesized that initial temporal delays between gestures and the corresponding words decrease and finally disappear so that gestural and verbal modalities coincide. It is suggested that engaging in communication in the presence of material has an important cognitive function in that it affords a distribution of cognition across different modalities until individuals have developed the competence to express themselves effectively in the verbal modality.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".