The Development of Morality and Conventionality Across Cultures: Implementing a Two‐Stage Model for Cross‐Cultural Research
Bibliographic record
Abstract
Establishing a shared sense of right and wrong is an essential milestone for human cooperation, raising the question of whether a universal set of moral intuitions exists. However, tests of universality in the domain of human morality are hindered by the overrepresentation of participants from Western, educated, industrialized, rich, and democratic (WEIRD) societies and issues of validity that arise from the use of WEIRD measures (i.e., measures originating in WEIRD societies and primarily normed on WEIRD samples) to make cross-cultural comparisons. Here we address the tension between cross-cultural generalizability and validity by deploying a two-stage approach to investigate the moral beliefs of 5- to 10-year-olds from four diverse societies (N = 331). Specifically, we test a classic case study in which strong universality claims have previously been made: the "moral/conventional" distinction. In Study 1, we test for the distinction cross-culturally using standardized measures widely used in moral cognition research and find robust evidence of the distinction in Canadian children, but a more variable pattern among Korean, Indian, and Iranian children, with Iranian children showing the weakest evidence for a distinction. In Study 2, we focus specifically on Iran and tailor experimental stimuli to reflect culture-specific norms in that country. We find that Iranian children residing under a theocracy also exhibit the moral/conventional distinction-so long as moral and conventional codes do not intersect with religious or legal concerns. These findings support the use of a two-stage model in which cultural comparisons are made using both shared and culturally specific measures. RESEARCH HIGHLIGHTS: Two studies examined whether children in four diverse societies make the distinction between violations of moral codes and social conventions. When standardized methods were used, evidence for the moral/conventional distinction was robust in Western children, variable in non-Western children, and weakest in Iranian children. Strong evidence for the moral/conventional distinction was found in Iranian children when culturally tailored measures were used. Our findings support the use of a two-stage model that combines the strengths of standardized and tailored measures for conducting cross-cultural research.
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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 | 0.065 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".