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Record W2059438841 · doi:10.1163/15685373-12342112

The Social Cost of Atheism: How Perceived Religiosity Influences Moral Appraisal

2014· article· en· W2059438841 on OpenAlexaff
Jennifer Cole Wright, Ryan Nichols

Bibliographic record

VenueJournal of Cognition and Culture · 2014
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReligiosityAtheismPsychologySocial psychologyStereotype (UML)Social cognitive theory of moralityMoral disengagementMoral psychologyMoralityMoral reasoningEpistemology

Abstract

fetched live from OpenAlex

Abstract Social psychologists have found that stereotypes correlate with moral judgments about agents and actions. The most commonly studied stereotypes are race/ethnicity and gender. But atheists compose another stereotype, one with its own ignominious history in the Western world, and yet, one about which very little is known. This project endeavored to further our understanding of atheism as a social stereotype. Specifically, we tested whether people with non-religious commitments were stereotypically viewed as less moral than people with religious commitments. We found that participants’ (both Christian and atheist) moral appraisals of atheists were more negative than those of Christians who performed the same moral and immoral actions. They also reported immoral behavior as more (internally and externally) consistent for atheists, and moral behavior more consistent for Christians. The results contribute to research at the intersection of moral theory, moral psychology, and psychology of religion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.073
GPT teacher head0.314
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2014
Admission routes1
Has abstractyes

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