The Place of Values in Virtue Science
Bibliographic record
Abstract
The central concern in this chapter is on the place of values and morality in virtue science. Since the advent of psychology, a strict fact–value dichotomy has predominated, with almost all investigators adopting a disengaged observer stance. This dichotomy has been repeatedly critiqued by communitarians, hermeneuticists, philosophers, and psychologists. Few, if any, systematic defenses of the fact–value dichotomy exist. This chapter combines many of the strands of fact–value critique in a neo-Aristotelian position that emphasizes that science is, itself, value-imbued because it aims at a set of goods (e.g., knowledge, human welfare). The chapter concludes by suggesting how values and morality can be included in virtue science and psychology in a frank and illuminating manner. In support of this position, it enumerates four advantages of value inclusion, paramount among them that values can then be explicitly discussed and evaluated. Values can be fruitfully incorporated into virtue and psychological sciences by making the values explicit and including discussions and critiques of those views in open intellectual discourse (e.g., peer review).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".