The nature and structure of correlations among Big Five ratings: The halo-alpha-beta model.
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".