It pays to be nice, but not really nice: Asymmetric reputations from prosociality across 7 countries
Bibliographic record
Abstract
Abstract Cultures differ in many important ways, but one trait appears to be universally valued: prosociality. For one’s reputation, around the world, it pays to be nice to others. However, recent research with American participants finds that evaluations of prosocial actions are asymmetric—relatively selfish actions are evaluated according to the magnitude of selfishness but evaluations of relatively generous actions are less sensitive to magnitude. Extremely generous actions are judged roughly as positively as modestly generous actions, but extremely selfish actions are judged much more negatively than modestly selfish actions (Klein & Epley, 2014). Here we test whether this asymmetry in evaluations of prosociality is culture-specific. Across 7 countries, 1,240 participants evaluated actors giving various amounts of money to a stranger. Along with relatively minor cross-cultural differences in evaluations of generous actions, we find cross-cultural similarities in the asymmetry in evaluations of prosociality. We discuss implications for how reputational inferences can enable the cooperation necessary for successful societies.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| 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".