Sociocultural Influences on Moral Judgments: East–West, Male–Female, and Young–Old
Bibliographic record
Abstract
Gender, age, and culturally specific beliefs are often considered relevant to observed variation in social interactions. At present, however, the scientific literature is mixed with respect to the significance of these factors in guiding moral judgments. In this study, we explore the role of each of these factors in moral judgment by presenting the results of a web-based study of Eastern (i.e., Russia) and Western (i.e., USA, UK, Canada) subjects, male and female, and young and old. Participants (n = 659) responded to hypothetical moral scenarios describing situations where sacrificing one life resulted in saving five others. Though men and women from both types of cultures judged (1) harms caused by action as less permissible than harms caused by omission, (2) means-based harms as less permissible than side-effects, and (3) harms caused by contact as less permissible than by non-contact, men in both cultures delivered more utilitarian judgments (save the five, sacrifice one) than women. Moreover, men from Western cultures were more utilitarian than Russian men, with no differences observed for women. In both cultures, older participants delivered less utilitarian judgments than younger participants. These results suggest that certain core principles may mediate moral judgments across different societies, implying some degree of universality, while also allowing a limited range of variation due to sociocultural factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".