The Strengths of Wisdom Provide Unique Contributions to Improved Leadership, Sustainability, Inequality, Gross National Happiness, and Civic Discourse in the Face of Contemporary World Problems
Bibliographic record
Abstract
We present evidence for the strengths of the intellectual virtues that philosophers and behavioral scientists characterize as key cognitive elements of wisdom. Wisdom has been of centuries-long interest for philosophical scholarship, but relative to intelligence largely neglected in public discourse on educational science, public policy, and societal well-being. Wise reasoning characteristics include intellectual humility, recognition of uncertainty, consideration of diverse viewpoints, and an attempt to integrate these viewpoints. Emerging scholarship on these features of wisdom suggest that they uniquely contribute to societal well-being, improve leadership, shed light on societal inequality, promote cooperation in Public Goods Games and reduce political polarization and intergroup-hostility. We review empirical evidence about macro-cultural, ecological, situational, and person-level processes facilitating and inhibiting wisdom in daily life. Based on this evidence, we speculate about ways to foster wisdom in education, organizations, and institutions.
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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 | 0.009 |
| 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 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| 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".