Bibliographic record
Abstract
This chapter outlines several threads of personality psychology to explore what it can tell us about virtue science. Personality psychology includes structural approaches (e.g., Big 5 and HEXACO), process approaches (e.g., the Cognitive-Affective Processing System or CAPS), integrations of structural and process approaches (e.g., Whole Trait Theory and the Three-Tiered Framework of Personality), and an emerging focus on systematic changes in personality through the lifespan. This research has clarified that traits exist and are measurable, that traits relate predictably to meaningful outcomes, that informant reports correlate to self-reports on personality, and that personality emerges cross-culturally. CAPS was developed to account for situational variation in personality expression. The systematic developmental changes in personality suggest that individuals mature as they adopt important roles in life, such as work and mating relationships. The integrative approaches to personality highlight its multidimensional nature and make it reasonable to consider merging the study of personality and virtue. The chapter concludes by arguing that virtues and personality dimensions are sufficiently different because virtue science emphasizes morality, choice, and practical wisdom, whereas personality theory and research do not. It suggests that traits may be best understood as a genus with at least two species: personality and virtue.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".