What do you learn about someone over time? The relationship between length of acquaintance and consensus and self-other agreement in judgments of personality.
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
Theory and research examining length of acquaintance and consensus among personality judgments have predominantly examined each dimension of personality separately. In L. J. Cronbach's (1955) terminology, this trait-centered approach combines consensus on elevation, differential elevation, and differential accuracy in personality judgments. The current article extends D. A. Kenny's (1991, 1994) weighted average model (WAM)--a theoretical model of the factors that influence agreement among personality judgments--to separate out two of Cronbach's components of consensus: stereotype accuracy and differential accuracy. Consistent with the predictions based on the WAM, as length of acquaintance increased, self-other agreement and consensus differential accuracy increased, stereotype accuracy decreased, and trait-level or raw profile correlations generally remained unchanged. Discussion focuses on the conditions under which a relationship between length of acquaintance and consensus and self-other agreement among personality evaluations emerges and how impressions change over time.
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 | 0.005 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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