Kin Cognition and Communication: What Talking, Gesturing, and Drawing About Family Can Tell us About the Way We Think About This Core Social Structure
Bibliographic record
Abstract
When people talk about kinship systems, they often use co-speech gestures and other representations to elaborate. This paper investigates such polysemiotic (spoken, gestured, and drawn) descriptions of kinship relations, to see if they display recurring patterns of conventionalization that capture specific social structures. We present an exploratory hypothesis-generating study of descriptions produced by a lesser-known ethnolinguistic community to the cognitive sciences: the Paamese people of Vanuatu. Forty Paamese speakers were asked to talk about their family in semi-guided kinship interviews. Analyses of the speech, gesture, and drawings produced during these interviews revealed that lineality (i.e., mother's side vs. father's side) is lateralized in the speaker's gesture space. In other words, kinship members of the speaker's matriline are placed on the left side of the speaker's body and those of the patriline are placed on their right side, when they are mentioned in speech. Moreover, we find that the gesture produced by Paamese participants during verbal descriptions of marital relations are performed significantly more often on two diagonal directions of the sagittal axis. We show that these diagonals are also found in the few diagrams that participants drew on the ground to augment their verbo-gestural descriptions of marriage practices with drawing. We interpret this behavior as evidence of a spatial template, which Paamese speakers activate to think and communicate about family relations. We therefore argue that extending investigations of kinship structures beyond kinship terminologies alone can unveil additional key factors that shape kinship cognition and communication and hereby provide further insights into the diversity of social structures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".