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Record W3146895620 · doi:10.1111/jopy.12635

Better to brag: Underestimating the risks of avoiding positive self‐disclosures in close relationships

2021· article· en· W3146895620 on OpenAlexfundno aff
Todd Chan, Zachary A. Reese, Oscar Ybarra

Bibliographic record

VenueJournal of Personality · 2021
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySocial psychologyExtraversion and introversionPerspective (graphical)EmpathyNeglectUnconditional positive regardPersonalityDevelopmental psychologyBig Five personality traits

Abstract

fetched live from OpenAlex

OBJECTIVE: Capitalization, or disclosing positive news in close relationships, is interpersonally and intrapersonally beneficial and expected by relational partners. Why do some individuals avoid capitalizing? How do close relational partners react when they later discover that positive news was not directly disclosed to them? METHOD: We conducted nine correlational and experimental studies using vignettes and recalled events (N = 2,177). RESULTS: We find that individuals who are concerned about being seen as braggarts tend to avoid capitalizing with their close relationships even when it is likely their partner would ultimately learn of the news. Yet this concern may be relatively unwarranted and these individuals show a forecasting error: They overestimate how negatively their partner would react to disclosure and predict that their partner would react more positively if they discovered the news through external means. However,they neglect to predict that partners who later learn of the news and realize they were not disclosed toward in fact feel devalued. We discuss how this concern with bragging is linked to decreased extraversion, perspective taking, and empathy. CONCLUSIONS: Uniquely in close relationships, being concerned about bragging may elicit negative relational outcomes, by hindering the positive self-disclosures that one's partners expect.

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)0.002
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 score0.029
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.123
GPT teacher head0.452
Teacher spread0.329 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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