The Emerging Science of Virtue
Bibliographic record
Abstract
Numerous scholars have claimed that positive ethical traits such as virtues are important in human psychology and behavior. Psychologists have begun to test these claims. The scores of studies on virtue do not yet constitute a mature science of virtue because of unresolved theoretical and methods challenges. In this article, we addressed those challenges by clarifying how virtue research relates to prosocial behavior, positive psychology, and personality psychology and does not run afoul of the fact–value distinction. The STRIVE-4 ( S calar T raits that are R ole sensitive, include Situation × Trait I nteractions, and are related to important V alues that help to constitute E udaimonia ) model of virtue is proposed to help resolve the theoretical and methods problems and encourage a mature science of virtue. The model depicts virtues as empirically verifiable, acquired scalar traits that are role sensitive, involve Situation × Trait interactions, and relate to important values that partly constitute eudaimonia (human flourishing). The model also holds that virtue traits have four major components: knowledge, behavior, emotion/motivation, and disposition. Heuristically, the STRIVE-4 model suggests 26 hypotheses, which are discussed in light of extant research to indicate which aspects of the model have been assessed and which have not. Research on virtues has included survey, intensive longitudinal, informant-based, experimental, and neuroscientific methods. This discussion illustrates how the STRIVE-4 framework can unify extant research and fruitfully guide future research.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".