A Philosophically Informed Virtue Science
Bibliographic record
Abstract
This chapter considers the appropriate roles that philosophy can play in virtue science. It develops three categories of philosophical work: (1) work that is not especially relevant to virtue science and can be set aside; (2) work that is relevant to virtue science, but is conceptual rather than empirical; and (3) philosophical topics that can be translated into testable hypotheses. Group 1 comprises the kind of philosophical work that seeks maximum generality, often transcending humans. This work can be set aside in virtue science, which is focused on human virtue. Group 2 includes philosophical work that is primarily conceptual and cannot be resolved empirically. This work includes many contentious premises on which virtue scientists may well have to take a position. We recommend that virtue scientists briefly discuss the issue and simply take a position without trying to resolve the issue. Group 3 includes philosophical positions that are amenable to empirical testing. Our recommendation is for virtue scientists to formulate and test such philosophical premises. For example, there is much philosophical debate about whether knowledge is important to virtue, and this debate can and should be tested empirically.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".