Toward Reconciling the Fragmentation of Virtue Science
Bibliographic record
Abstract
This chapter begins with a brief review of the history of virtue science. It went out of favor in psychology for most of the twentieth century, but after renewed interest in philosophy in the latter part of that century, virtue research has burgeoned in psychology in the twenty-first century. The interest in virtue research is partly due to the positive psychology movement, which focuses on human strengths and well-being. Despite its valuable contribution, three elements of positive psychology have continued to plague virtue research as it is atheoretical, conceptualized as a diagnostic scheme, and ambivalent about values and morality. Nevertheless, virtue science is off to a good beginning, boasting scores of empirical studies. Most virtue scientists have left the ill-conceived notion of “diagnosing” virtues behind. These studies remain siloed and noncumulative due to the absence of clear theory in virtue science and a tendency to neglect conceptualizing virtues. Virtue research also remains limited by its ambivalence toward values and morality. To remedy this fragmentation, this chapter proposes the STRIVE-4 Model, with its clear conceptualization of virtues and the dozens of hypotheses that follow from it. This model provides a way to build a unified and cumulative virtue science.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".