Values and Eudaimonia as Guideposts for Virtues
Bibliographic record
Abstract
Some neo-Aristotelians see a strong link between virtues and eudaimonia or flourishing, but others do not. After acknowledging this difference, the chapter explores some of the possible implications of this link. The view explored in this chapter is that virtues contribute to success in goal and good pursuit, which, in turn, contributes to a flourishing life. The neo-Aristotelian view examined holds that there are things that are good for humans qua humans (e.g., close personal relationships, group belonging). Success in pursuing these goods is hypothesized to be correlated with eudaimonia. It explores several challenges in studying eudaimonia, but concludes that eudaimonia research should continue and be updated as conceptualization and measurement improves. The chapter concludes with a discussion of three well-documented human goods (close personal relationships, group belonging, and meaning) and their hypothesized relationships with specific virtues (e.g., loyalty, forgiveness, honesty).
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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 | 0.001 | 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 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".