MétaCan
Menu
Back to cohort
Record W3092083095 · doi:10.1177/0146167218794631

A World of Blame to Go Around: Cross-Cultural Determinants of Responsibility and Punishment Judgments

2018· article· en· W3092083095 on OpenAlexaff
Matthew Feinberg, Ray Fang, Shi Liu, Kaiping Peng

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2018
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial psychologyAttributionPsychologyBlameHarmCollectivismPunishment (psychology)PerceptionAccountabilityAgency (philosophy)IndividualismPolitical scienceSociology

Abstract

fetched live from OpenAlex

Research finds collectivists make external attributions for others' behavior, whereas individualists make internal attributions. By focusing on external causes, collectivists should be less punitive toward those who harm others. Yet, many collectivistic cultures are known for strict retributive justice systems. How can collectivists simultaneously make external attributions and punish so harshly? We hypothesized that unlike individualists whose analytic tendencies engender a focus on mental states where judgments of accountability stem from perceptions of a harm-doer's agency, collectivists' holistic cognitive tendencies engender a focus on social harmony where judgments of accountability stem from perceived social consequences of the harmful act. Thus, what leads collectivists to make external attributions for behavior also leads to harsh punishment of those harming the collective welfare. Four cross-cultural studies found evidence that perceptions of a target's agency more strongly predicted responsibility and punishment judgments for individualists, whereas perceived severity of the harm was stronger for collectivists.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.457
Teacher spread0.337 · 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.

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

Citations57
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePersonality and Social Psychology BulletinSame topicCultural Differences and ValuesFrench-language works237,207