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Record W3036322498 · doi:10.1177/1948550620931985

Wise Reasoning About the Future Is Associated With Adaptive Interpersonal Feelings After Relational Challenges

2020· article· en· W3036322498 on OpenAlexafffund
Johanna Peetz, Igor Grossmann

Bibliographic record

VenueSocial Psychological and Personality Science · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of WaterlooCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsClosenessPsychologySocial psychologyInterpersonal communicationFeelingMeaning (existential)Interpersonal perceptionInterpersonal relationshipPerceptionSocial perceptionPsychotherapist

Abstract

fetched live from OpenAlex

Two prospective studies examine how wise reasoning about an anticipated conflict interaction—intellectual humility, recognition of uncertainty and change, consideration and integration of different perspectives—is associated with interpersonal feelings and closeness to the interaction partner after the challenging interaction. In Study 1 ( N = 243) and preregistered replication Study 2 ( N = 234), participants who reasoned more wisely before an anticipated conflict interaction felt more positively toward and closer to the person involved in the conflict afterward. We explored three avenues accompanying effects of wise reasoning about the future for interpersonal outcomes: conflict outcome, perception of the interaction as fair and satisfying, and sense of meaning in the conflict experience. Path models indicated that sense of meaning was a consistent factor accounting for the positive effect of prospective wise reasoning for relational well-being. We discuss implications for research on prospection, wisdom, and well-being.

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)0.015
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
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 integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.338
Teacher spread0.254 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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