The Needs of the Many Do Not Outweigh the Needs of the Few: The Limits of Individual Sacrifice across Diverse Cultures
Bibliographic record
Abstract
Abstract A long tradition of research in WEIRD (Western, Educated, Industrialized, Rich, Democratic) countries has investigated how people weigh individual welfare versus group welfare in their moral judgments. Relatively less research has investigated the generalizability of results across non-WEIRD populations. In the current study, we ask participants across nine diverse cultures (Bali, Costa Rica, France, Guatemala, Japan, Madagascar, Mongolia, Serbia, and the USA ) to make a series of moral judgments regarding both third-party sacrifice for group welfare and first-person sacrifice for group welfare. In addition to finding some amount of cross-cultural variation on most of our questions, we also find two cross-culturally consistent judgments: (1) when individuals are in equivalent situations, overall welfare should be maximized, and (2) harm to individuals should be taken into account, and some types of individual harm can trump overall group welfare. We end by discussing the specific pattern of variable and consistent features in the context of evolutionary theories of the evolution of morality.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".