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
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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