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Record W2036853093 · doi:10.1037/a0030931

For whom do the ends justify the means? Social class and utilitarian moral judgment.

2012· article· en· W2036853093 on OpenAlexafffund
Stéphane Côté, Paul K. Piff, Robb Willer

Bibliographic record

VenueJournal of Personality and Social Psychology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
FundersRotman School of Management, University of Toronto
KeywordsEmpathyPsychologySocial psychologyMoralityDilemmaSocial dilemmaUtilitarianismUpper classClass (philosophy)Social classAssociation (psychology)Moral developmentMoral reasoningEpistemologySociologyEconomics

Abstract

fetched live from OpenAlex

Though scholars have speculated for centuries on links between individuals' social class standing and approach to moral reasoning, little systematic research exists on how class and morality are associated. Here, we investigate whether the tendency of upper-class individuals to exhibit reduced empathy makes them more likely to resist intuitionist options in moral dilemmas, instead favoring utilitarian choices that maximize the greatest good for the greatest number. In Study 1, upper-class participants were more likely than lower-class participants to choose the utilitarian option in the footbridge dilemma, which evokes relatively strong moral intuitions, but not in the standard trolley dilemma, which evokes relatively weak moral intuitions. In Study 2, upper-class participants were more likely to take resources from one person to benefit several others in an allocation task, and this association was explained by their lower empathy for the person whose resources were taken. Finally, in Study 3, the association between social class and utilitarian judgment was reduced in a condition in which empathy was induced, but not in a control condition, suggesting that reduced empathy helps account for the utilitarianism of upper-class individuals.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.239
GPT teacher head0.377
Teacher spread0.138 · 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

Citations202
Published2012
Admission routes2
Has abstractyes

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