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Record W2509417944 · doi:10.1177/1948550616662127

Disagreement About Moral Character Is Linked to Interpersonal Costs

2016· article· en· W2509417944 on OpenAlexaff
Maxwell Barranti, Erika N. Carlson, R. Michael Furr

Bibliographic record

VenueSocial Psychological and Personality Science · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySocial psychologyHonestyCharacter (mathematics)Moral characterAgreeablenessPersonalityInterpersonal communicationCompassionMoral developmentInterpersonal relationshipBig Five personality traitsExtraversion and introversion

Abstract

fetched live from OpenAlex

Impressions of moral character are among the most relevant and consequential; yet, people do not always see eye to eye with others about their moral character. Is self-other disagreement about moral character associated with interpersonal costs, and are these costs uniquely associated with moral impressions? To answer these questions, judges ( N = 100) in a community sample rated several acquaintances’ (targets) moral character (e.g., compassion, honesty) and personality and indicated their liking and respect of the target ( N = 587 judge–target pairs) while targets described their own moral character and personality. For most moral impressions, as the discrepancy between judges and targets increased, judges tended to like and respect targets less, particularly when targets enhanced their character relative to their judge. These effects were unique from personality ratings (e.g., agreeableness). Thus, failing to see eye to eye with others about one’s moral character is associated with negative interpersonal outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.218
GPT teacher head0.380
Teacher spread0.162 · 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

Citations30
Published2016
Admission routes1
Has abstractyes

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