Why are well-adjusted people seen more accurately? The role of personality-behavior congruence in naturalistic social settings.
Bibliographic record
Abstract
Expressive accuracy, being viewed in line with one's unique, distinctive personality traits, is emerging as an important individual difference that is strongly linked to psychological well-being. Yet little is known about what underlies expressive accuracy and its associations with well-being. The current studies examined whether personality-behavior congruence, the tendency to behave in line with one's distinctive personality trait profile, contributes to the links between well-being and expressive accuracy with new acquaintances (Unique perceiver-target pairs: Study 1: N = 437; Study 2: N = 874), by assessing congruence in naturalistic situations, including in a series of getting-acquainted interactions (Study 1; Ntargets = 77; Mdn Interactions: 7) and social situations in daily life over a 2-week period (Study 2; Ntargets = 146; MdnAssessments: 49). Across studies, we found that greater well-being predicted greater congruence, in both naturalistic social interactions and in daily life, which in turn contributed to greater expressive accuracy in getting-acquainted interactions. Overall, the current studies demonstrate the important role that congruence plays in expressive accuracy, helping to explain why well-adjusted individuals are seen more accurately. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".