Blissfully Blind or Painfully Aware? Exploring the Beliefs People With Interpersonal Problems Have About Their Reputation
Bibliographic record
Abstract
OBJECTIVE: Problematic interpersonal behavior might stem from and be maintained by the beliefs people have about how others see them (i.e., metaperceptions). The current study tested whether people with interpersonal problems formed more or less accurate metaperceptions about their personality (meta-accuracy), whether they thought others saw them in more or less positive ways (positivity), and whether they underestimated or overestimated how much others saw them as they saw themselves (transparency). METHOD: = 19.78; 36% male) completed a measure of interpersonal problems and provided personality judgments and metaperceptions for a group of peers after a first impression and after 4 months of acquaintanceship. RESULTS: Generalized distress was associated with less positive metaperceptions at both times and with higher meta-accuracy after 4 months. Dominance problems were not associated with meta-accuracy, positivity, or transparency after a first impression, but dominance was linked to lower meta-accuracy and lower positivity after 4 months. Affiliation problems were associated with higher meta-accuracy after a first impression and with higher positivity and transparency at both times. CONCLUSIONS: Metaperceptions were linked to interpersonal problems, and these expectations might partially explain some maladaptive patterns of behavior.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".