Geocentric Gestures as a Research Tool
Bibliographic record
Abstract
This study1 is part of a large-scale cross-cultural research project on the development of spatial language and cognition, in India, Indonesia and Nepal, that focuses on a culturally particular way of organizing small-scale, table space, using a large-scale geocentric spatial orientation system (Dasen & Mishra, in preparation). One of the main questions is at what age this geocentric frame of reference starts to be effective. The study of language development does not provide a clear answer, because young children (ages 4 to 7) use ambiguous “deictic” descriptions, i.e., they just say “this way” accompanied by a gesture. Can these gestures be used to clarify the meaning of language? To answer this question, 234 video recordings of Nepalese children performing the “Perspectives” task (in which they have to describe the location of three objects placed on a table in front of them, under three different conditions) were analyzed separately for both language and gestures. The results show a good correspondence between language and gestures in 9 to 12 year olds. This allows us to interpret further the frame of reference used by the younger children. Out of 367 items on which young children (4 to 9 years) give an ambiguous deictic answer, only 17% are accompanied with an egocentric gesture, and 83% with a geocentric one (combining 48% large gestures linked to the use of cardinal directions, and 35% medium-large gestures linked to the use of situational local landmarks). This shows that a geocentric frame is at play as early as age 4, even when the child cannot express it clearly in the 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".