Bibliographic record
Abstract
When individuals trade with strangers, there is a temptation to renege on con-tracts. In the absence of repeated interaction or exogenous enforcement mechanisms, this problem can impede valuable exchange. Historically, individuals have solved this problem by forming institutions that sustain trade using group, rather than individ-ual, reputation. Groups can employ two mechanisms to uphold reputation that are generally unavailable to isolated individuals: information sharing and in-group pun-ishment. In this paper, we design a laboratory experiment to distinguish the roles of these two mechanisms in sustaining group reputation and increasing gains from trade. We find that information sharing encourages path dependence via group reputation; good (bad) behavior by individuals results in greater (fewer) gains from exchange for the group in the future. However, the mere threat of in-group punishment is enough to discourage bad behavior, even if punishment is rarely employed. When combined, information sharing and in-group punishment work as complements; the presence of in-group punishment encourages cooperation early on, and information sharing reinforces
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".