The Wisdom in Virtue: Pursuit of Virtue Predicts Wise Reasoning About Personal Conflicts
Bibliographic record
Abstract
Most people can reason relatively wisely about others' social conflicts, but often struggle to do so about their own (i.e., Solomon's paradox). We suggest that true wisdom should involve the ability to reason wisely about both others' and one's own social conflicts, and we investigated the pursuit of virtue as a construct that predicts this broader capacity for wisdom. Results across two studies support prior findings regarding Solomon's paradox: Participants ( N = 623) more strongly endorsed wise-reasoning strategies (e.g., intellectual humility, adopting an outsider's perspective) for resolving other people's social conflicts than for resolving their own. The pursuit of virtue (e.g., pursuing personal ideals and contributing to other people) moderated this effect of conflict type. In both studies, greater endorsement of the pursuit of virtue was associated with greater endorsement of wise-reasoning strategies for one's own personal conflicts; as a result, participants who highly endorsed the pursuit of virtue endorsed wise-reasoning strategies at similar levels for resolving their own social conflicts and resolving other people's social conflicts. Implications of these results and underlying mechanisms are explored and discussed.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".