The emergence of sharing networks through indirect signaling
Bibliographic record
Abstract
Communities around the world rely on networks of resource sharing to buffer households against hardship. Yet, under such informal insurance schemes, some needy families can be systematically left out. By modeling sharing and reputational spread on a network, we show how generosity can be sustained when reputational benefits spread across a community’s communication network, but also how network density and position shape who receives transfers---well-connected households become priority receivers due to their ability to spread givers' reputation more effectively. Our analysis, combining mathematical modeling with food sharing data from an Inuit community, reveals that communication network sparsity incentivizes broader sharing, but also more selectivity. The correlation of communication network structure and resource endowment is critical for stabilizing needs-based transfers. If need status does not perfectly (negatively) correlate with social influence, marginal households in need may still be excluded from sharing. By linking individual incentives to community-wide patterns, our framework clarifies when indirect reciprocity can stabilize outcomes that show the signatures of need-based transfers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".