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Record W2920326543 · doi:10.1037/emo0000588

Nudging the better angels of our nature: A field experiment on morality and well-being.

2019· article· en· W2920326543 on OpenAlexaff
Adam Waytz, Wilhelm Hofmann

Bibliographic record

VenueEmotion · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPsychologyMoralityEmpathySocial psychologyClosenessMoral developmentPsycINFOSocial cognitive theory of moralityMoral disengagementDevelopmental psychologyMEDLINEEpistemology

Abstract

fetched live from OpenAlex

A field experiment examines how moral behavior, moral thoughts, and self-benefiting behavior affect daily well-being. Using experience sampling technology, we randomly grouped participants over 10 days to either behave morally, have moral thoughts, or do something positive for themselves. Participants received treatment-specific instructions in the morning of 5 days and no instructions on the other 5 control days. At each day's end, participants completed measures that examined, among others, subjective well-being, self-perceived morality and empathy, and social isolation and closeness. Full analyses found limited evidence for treatment- versus control-day differences. However, restricting analyses to occasions on which participants complied with instructions revealed treatment- versus control-day main effects on all measures, while showing that self-perceived morality and empathy toward others particularly increased in the moral deeds and moral thoughts group. These findings suggest that moral behavior, moral thoughts, and self-benefiting behavior are all effective means of boosting well-being, but only moral deeds and, perhaps surprisingly, also moral thoughts strengthen the moral self-concept and empathy. Results from an additional study assessing laypeople's predictions suggest that people do not fully intuit this pattern of results. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.594

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 studies0.0000.000
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 head0.013
GPT teacher head0.320
Teacher spread0.306 · 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.

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

Citations21
Published2019
Admission routes1
Has abstractyes

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