What defines Traits, Reputations, and Identity? Personality item content in multi-rater judgments
Bibliographic record
Abstract
Personality self- and informant-reports have been ascribed complementary value based on the asymmetric knowledge of the two perspectives. However, this study is the first to investigate what personality (item) content is reflected in the shared and unique components in multi-rater personality judgments. In two large data sets (Sample 1: 664 targets/1,615 informants; Sample 2: 478 targets/1,434 informants), we used latent variable models to separate judgments into variance that is shared across targets and informants (the Trait factor), unique to self-reports (Identity), and unique to informant-reports (Reputation). Then, we predicted the personality items’ loadings for each factor from the items’ content. This included items’ affective, behavioral, cognitive, or desire-related content, observability and evaluativeness, and centrality to identity or reputation. We found that Trait consensus was generally promoted by items reflecting observable, behavioral, but also affective content. Unique self-perceptions were captured especially by cognitions and non-observable content. Evaluativeness had inconsistent effects across samples. Similarly, unique informant-views reflected different content across samples. Both may depend on the types of informants or the available item sample. These insights build the foundation for leveraging the power of multi-rater perspectives on personality for advancing theory and measurement across different perspectives.
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.
How this classification was reachedexpand
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".