The nature and structure of correlations among Big Five ratings: The halo-alpha-beta 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
In light of consistently observed correlations among Big Five ratings, the authors developed and tested a model that combined E. L. Thorndike's (1920) general evaluative bias (halo) model and J. M. Digman's (1997) higher order personality factors (alpha and beta) model. With 4 multitrait-multimethod analyses, Study 1 revealed moderate convergent validity for alpha and beta across raters, whereas halo was mainly a unique factor for each rater. In Study 2, the authors showed that the halo factor was highly correlated with a validated measure of evaluative biases in self-ratings. Study 3 showed that halo is more strongly correlated with self-ratings of self-esteem than self-ratings of the Big Five, which suggests that halo is not a mere rating bias but actually reflects overly positive self-evaluations. Finally, Study 4 demonstrated that the halo bias in Big Five ratings is stable over short retest intervals. Taken together, the results suggest that the halo-alpha-beta model integrates the main findings in structural analyses of Big Five correlations. Accordingly, halo bias in self-ratings is a reliable and stable bias in individuals' perceptions of their own attributes. Implications of the present findings for the assessment of Big Five personality traits in monomethod studies are discussed.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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