Thirty Years of Psychological Wisdom Research: What We Know About the Correlates of an Ancient Concept
Bibliographic record
Abstract
Psychologists have studied the ancient concept of wisdom for 3 decades. Nevertheless, apparent discrepancies in theories and empirical findings have left the nomological network of the construct unclear. Using multilevel meta-analyses, we summarized wisdom's correlations with age, intelligence, the Big Five personality traits, narcissism, self-esteem, social desirability, and well-being. We furthermore examined whether these correlations were moderated by the general approach to conceptualizing and measuring wisdom (i.e., phenomenological wisdom as indexed by self-report vs. performative wisdom as indexed by performance ratings), by specific wisdom measures, and by variable-specific factors (e.g., age range, type of intelligence measures, and well-being type). Although phenomenological and performative approaches to conceptualizing and measuring wisdom had some unique correlates, both were correlated with openness, hedonic well-being, and eudaimonic well-being, especially the growth aspect of eudaimonic well-being. Differences between phenomenological and performative wisdom are discussed in terms of the differences between typical and maximal performance, self-ratings and observer ratings, and global and state wisdom. This article will help move the scientific study of wisdom forward by elucidating reliable wisdom correlates and by offering concrete suggestions for future empirical research based on the meta-analytic findings.
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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 | 0.023 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".