Dispositional pathways to trust: Self-esteem and agreeableness interact to predict trust and negative emotional disclosure.
Bibliographic record
Abstract
Expressing our innermost thoughts and feelings is critical to the development of intimacy (Reis & Shaver, 1988), but also risks negative evaluation and rejection. Past research suggests that people with high self-esteem are more expressive and self-disclosing because they trust that others care for them and will not reject them (Gaucher et al., 2012). However, feeling good about oneself may not always be enough; disclosure may also depend on how we feel about other people. Drawing on the principles of risk regulation theory (Murray et al., 2006), we propose that agreeableness-a trait that refers to the positivity of interpersonal motivations and behaviors-is a key determinant of trust in a partner's caring and responsiveness, and may work in conjunction with self-esteem to predict disclosure. We examined this possibility by exploring how both self-esteem and agreeableness predict a particularly risky and intimate form of self-disclosure, the disclosure of emotional distress. In 6 studies using correlational, partner-report, and experimental methods, we demonstrate that self-esteem and agreeableness interact to predict disclosure: People who are high in both self-esteem and agreeableness show higher emotional disclosure. We also found evidence that trust mediates this effect. People high in self-esteem and agreeableness are most self-revealing, it seems, because they are especially trusting of their partners' caring. Self-esteem and agreeableness were particularly important for the disclosure of vulnerable emotions (i.e., sadness; Study 5) and disclosures that were especially risky (Study 6). These findings illustrate how dispositional variables can work together to explain behavior in close relationships. (PsycINFO Database Record
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".