Meta-accuracy and relationship quality: Weighing the costs and benefits of knowing what people really think about you.
Bibliographic record
Abstract
People use metaperceptions, or their beliefs about how other people perceive them, to initiate and maintain social bonds. Are accurate metaperceptions associated with higher quality relationships? In four studies, the current research answers this question but considers the possibility that the self might not experience the same relational benefits of accurate metaperceptions, or meta-accuracy, as the people who form judgments about the self. For example, people tend to like individuals who have accurate self-perceptions, yet individuals tend to enjoy their own relationships more with people they believe see them in desirable ways. To test whether meta-accuracy is linked to relationship quality and whether this link differs for the self and others, meta-accuracy for personality traits as well as metaperceiver- and judge-reported relationship quality were assessed among new acquaintances (N = 184), peers (N = 228), friends (N = 273), and romantic partners (N = 401). Results suggested that judges enjoyed relationships more with metaperceivers who knew the impression they made, regardless of whether judges' impressions were desirable (i.e., positive or self-verifying). Initial meta-accuracy also predicted greater relationship quality over time, suggesting that accurate metaperceptions have positive effects on relationships. In contrast, rather than enjoying relationships more when they were accurate, metaperceivers enjoyed relationships more when they believed judges perceived them in positive or self-verifying ways. Thus, meta-accuracy seems to be a virtue in the eyes of judges, but metaperceivers do not seem to reap the same benefits of knowing what others really think. Implications for improving meta-accuracy are discussed. (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 | 0.012 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".