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Record W2059720928 · doi:10.1177/1948550614534698

Religion and Punishment

2014· article· en· W2059720928 on OpenAlexaff
Kristin Laurin, Jason E. Plaks

Bibliographic record

VenueSocial Psychological and Personality Science · 2014
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReligiosityPsychologySocial psychologyOrthodoxyPunishment (psychology)Action (physics)Priming (agriculture)

Abstract

fetched live from OpenAlex

We hypothesize that two distinct facets of religiosity—orthodoxy (an emphasis on belief) and orthopraxy (an emphasis on behavior)—predict differential sensitivity to an actor’s intent when making moral judgments. Participants judged actors who performed misdeeds intentionally or unintentionally. In Study 1, high orthopraxy predicted harsher judgments of the unintentional actor, while high orthodoxy predicted more lenient judgments. In Study 2, we investigated a potential explanation for these effects, priming participants with either an “action focus” or a “thought focus.” Action-focused participants judged the unintentional actor more harshly than did thought-focused participants. In Study 3, participants from an orthopraxic tradition (Hinduism) judged the unintentional actor more harshly than did those from an orthodox tradition (Protestantism). These findings contribute to a growing literature on the multifaceted nature of religion. They also carry broader implications for understanding people’s responses to actions as a function of the actor’s mental state.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.152
GPT teacher head0.356
Teacher spread0.204 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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