A multi-rater framework for studying personality: The trait-reputation-identity model.
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
Personality and social psychology have historically been divided between personality researchers who study the impact of traits and social-cognitive researchers who study errors in trait judgments. However, a broader view of personality incorporates not only individual differences in underlying traits but also individual differences in the distinct ways a person's personality is construed by oneself and by others. Such unique insights are likely to appear in the idiosyncratic personality judgments that raters make and are likely to have etiologies and causal force independent of trait perceptions shared across raters. Drawing on the logic of the Johari window (Luft & Ingham, 1955), the Self-Other Knowledge Asymmetry Model (Vazire, 2010), and Socioanalytic Theory (Hogan, 1996; Hogan & Blickle, 2013), we present a new model that separates personality variance into consensus about underlying traits (Trait), unique self-perceptions (Identity), and impressions conveyed to others that are distinct from self-perceptions (Reputation). We provide three demonstrations of how this Trait-Reputation-Identity (TRI) Model can be used to understand (a) consensus and discrepancies across rating sources, (b) personality's links with self-evaluation and self-presentation, and (c) gender differences in traits. We conclude by discussing how researchers can use the TRI Model to achieve a more sophisticated view of personality's impact on life outcomes, developmental trajectories, genetic origins, person-situation interactions, and stereotyped judgments. (PsycINFO Database Record
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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