<scp>BaYaka</scp> forager food sharing networks in the Congo Basin: The roles of gender homophily and kin sharing
Bibliographic record
Abstract
Abstract Objectives Food sharing is a costly form of cooperation that was likely critical to human evolutionary success, including the emergence of human's life history strategy. Food sharing in human communities may be maintained through a number of pathways, including direct dyadic reciprocity, reputation‐based processes, and kin‐biased exchange. Differences in reproductive demands, labor, and cultural norms may also result in gendered differences in cooperative networks. Here, we examine cooperative networks in egalitarian BaYaka foragers from the Congo Basin. Materials and Methods We collected social network data from 112 adults in 41 households in this subsistence community. We implement a Bayesian latent network model to assess individual‐, dyadic‐, and block‐level predictors of food sharing partners. Results Conditioning on covariates, we found limited evidence for direct dyadic reciprocity in food sharing. Despite local norms regarding prestige avoidance, we found status‐based homophily. High‐status individuals—council members and local healers—were more likely to share with one another. Importantly, our results highlight gender differences in patterns of food sharing, interacting with genetic relatedness. Women were more likely to share with one another, especially with kin as genetic relatedness increased. Discussion Our results align with evolutionary framing emphasizing kin selection in costly cooperation. The results showing that women cooperate with other women, particularly kin, also complement sex‐based patterns in some other mammalian species, potentially reflecting the social support necessary to manage reproductive costs and childcare. BaYaka women's subsistence productivity and local cultural dynamics for autonomy and egalitarianism may likewise help facilitate women's preferential cooperation with one another.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".