Prosocial behavior leads to happiness in a small-scale rural society.
Bibliographic record
Abstract
Humans are extraordinarily prosocial, and research conducted primarily in North America indicates that giving to others is emotionally rewarding. To examine whether the hedonic benefits of giving represent a universal feature of human behavior, we extended upon previous cross-cultural examinations by investigating whether inhabitants of a small-scale, rural, and isolated village in Vanuatu, where villagers have little influence from urban, Western culture, survive on subsistence farming without electricity, and have minimal formal education, report or display emotional rewards from engaging in prosocial (vs. personally beneficial) behavior. In Study 1, adults were randomly assigned to purchase candy for either themselves or others and then reported their positive affect. Consistent with previous research, adults purchasing goods for others reported greater positive emotion than adults receiving resources for themselves. In Study 2, 2- to 5-year-old children received candy and were subsequently asked to engage in costly giving (sharing their own candy with a puppet) and non-costly giving (sharing the experimenter's candy with a puppet). Emotional expressions were video-recorded during the experiment and later coded for happiness. Consistent with previous research conducted in Canada, children displayed more happiness when giving treats away than when receiving treats themselves. Moreover, the emotional rewards of giving were largest when children engaged in costly (vs. non-costly) giving. Taken together, these findings indicate that the emotional rewards of giving are detectable in people living in diverse societies and support the possibility that the hedonic benefits of generosity are universal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".