Virtue, well-being, and mentalized affectivity
Bibliographic record
Abstract
Virtue ethics, featuring the claim that virtue leads to wellbeing, has been imported by psychologists from philosophy. In the first part of the paper, we re-examine the source of virtue ethics in Aristotle's philosophy and question whether virtues can be the path to eudaimonistic well-being for us, given that contemporary society differs from ancient society in terms of a lack of consensus about virtues. We focus on the modulation of emotions as a good starting place for reconstruing virtue ethics, and we affirm a connection to well-being through the construct of "mentalized affectivity", which is a specific kind of emotion regulation. In the second half of this hybrid paper, we provide evidence for the link between mentalized affectivity and well-being, based upon an empirical study with an adult sample (N=558). Our study examined how the Mentalized Affectivity Scale (MAS) predicts subjective well-being compared to five commonly used and related measures: Difficulty with Emotion Regulation Scale; Emotion Regulation Questionnaire; Flexibility Regulation of Emotional Expression scale; Reflective Functioning Questionnaire; Toronto Alexithymia Scale. The most important finding is that the MAS and Difficulties in Emotion Regulation Scale are most predictive of satisfaction with life. A second finding, less relevant for the present paper, is that the MAS (namely, its components of Identifying and Processing) strongly predicted psychopathology, including anxiety and mood disorders. This suggests that the MAS is a valuable tool for research on emotion regulation, well-being, and psychopathology, and that mentalized affectivity ought to be regarded as a promising construct for re-describing and specifying the contemporary relevance of virtue ethics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".