An Examination of Information Quality as a Moderator of Accurate Personality Judgment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it