Better to brag: Underestimating the risks of avoiding positive self‐disclosures in close relationships
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".