An Examination of Information Quality as a Moderator of Accurate Personality Judgment
Bibliographic record
Abstract
Information quality is an important moderator of the accuracy of personality judgment, and this article describes research focusing on how specific kinds of information are related to accuracy. In this study, 228 participants (159 female, 69 male; mean age = 23.43; 86.4% Caucasian) in unacquainted dyads were assigned to discuss thoughts and feelings, discuss behaviors, or engage in behaviors. Interactions lasted 25-30 min, and participants provided ratings of their partners and themselves following the interaction on the Big Five traits, ego-control, and ego-resiliency. Next, the amount of different types of information made available by each participant was objectively coded. The accuracy criterion, composed of self- and acquaintance ratings, was used to assess distinctive and normative accuracy using the Social Accuracy Model. Participants in the discussion conditions achieved higher distinctive accuracy than participants who engaged in behaviors, but normative accuracy did not differ across conditions. Information about specific behaviors and general behaviors were among the most consistent predictors of higher distinctive accuracy. Normative accuracy was more likely to decrease than increase when higher-quality information was available. Verbal information about behaviors is the most useful for learning about how people are unique.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".