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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 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)0.002
metaresearch head score (Gemma)0.035
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.218
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 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

Citations30
Published2016
Admission routes1
Has abstractyes

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