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Record W2144534243 · doi:10.1111/spc3.12154

How Emotions Shape Moral Behavior: Some Answers (and Questions) for the Field of Moral Psychology

2015· article· en· W2144534243 on OpenAlex
Rimma Teper, Chen‐Bo Zhong, Michael Inzlicht

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial and Personality Psychology Compass · 2015
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySocial cognitive theory of moralityMoral disengagementMoral psychologyMoral behaviorField (mathematics)Moral reasoningSocial psychologyMoral developmentMoral authority

Abstract

fetched live from OpenAlex

Abstract Within the past decade, the field of moral psychology has begun to disentangle the mechanics behind moral judgments, revealing the vital role that emotions play in driving these processes. However, given the well‐documented dissociation between attitudes and behaviors, we propose that an equally important issue is how emotions inform actual moral behavior – a question that has been relatively ignored up until recently. By providing a review of recent studies that have begun to explore how emotions drive actual moral behavior, we propose that emotions are instrumental in fueling real‐life moral actions. Because research examining the role of emotional processes on moral behavior is currently limited, we push for the use of behavioral measures in the field in the hopes of building a more complete theory of real‐life moral behavior.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.585

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 head0.286
GPT teacher head0.412
Teacher spread0.126 · 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