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Record W2509385610 · doi:10.3389/fpsyg.2016.01334

Sociocultural Influences on Moral Judgments: East–West, Male–Female, and Young–Old

2016· article· en· W2509385610 on OpenAlexaboutno aff
K.R. Arutyunova, Yuri I. Alexandrov, Marc D. Hauser

Bibliographic record

VenueFrontiers in Psychology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsnot available
FundersRussian Academy of SciencesRussian Science Foundation
KeywordsPsychologySociocultural evolutionSocial psychologyVariation (astronomy)Developmental psychologySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.305
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations71
Published2016
Admission routes1
Has abstractyes

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