Bibliographic record
Abstract
The book concludes with a discussion of the ways that virtue science can influence the discipline of psychology. First, it reiterates that virtue science is off to a good start. The success of virtue science calls the fact–value dichotomy into question because scientifically studying virtues is deeply imbued with value commitments. Virtue science is more interdisciplinary than psychology, and the value of working across disciplinary lines in virtue science recommends greater interdisciplinarity among psychologists. This interdisciplinarity in virtue science has helped to clarify the many philosophical contentions that tend to be ignored by psychologists or just built in as contentious assumptions. The STRIVE-4 Model clarifies how much improved conceptualization can enhance a research area, suggesting that psychology, as a discipline, can benefit from more systematic theory. Virtue science also calls for improved research, especially person-centered research and transcending self-report measures. Finally, virtue science calls for the recognition of the centrality of the aspiration to live well as human beings. Greater attention to this core aim can help psychologists to be much clearer and more direct about their objectives.
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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 | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".