What is the structure of perceiver effects? On the importance of global positivity and trait-specificity across personality domains and judgment contexts.
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
= 2,199 perceivers judged others on several trait domains (i.e., the Big Five, agency & communion) and in different judgment contexts (i.e., level of involvement with targets, level of exposure to targets). Results suggest that perceiver effects are hierarchically structured such that they reflect both a global tendency to view others positively versus negativity and specific tendencies to view others as high or low with respect to trait content. The relative importance of these components varied considerably across trait domains and judgment contexts: Perceiver effects were more specific for traits higher in observability and lower in evaluativeness and in context with less personal involvement and higher exposure to targets. Overall, results provide strong evidence for the hierarchical structure of perceiver effects and suggest that their meaning systematically varies depending on trait domain and possibly the judgment context. Implications for theory and assessment are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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 | 0.001 | 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 | 0.000 |
| 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