What Are the Necessary Conditions for Wisdom? Examining Intelligence, Creativity, Meaning-Making, and the Big-Five Traits
Why this work is in the frame
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Bibliographic record
Abstract
We investigated whether intelligence, creativity, meaning-making, and the Big-Five traits are necessary conditions for wisdom. We used Amazon’s TurkPrime to recruit 298 participants who ranged from 20 to 73 years of age. Participants completed measures of intelligence, creativity, meaning-making, and the Big-Five traits, along with a battery of self-report and performance wisdom measures. We used principal component analyses to reduce the wisdom battery into self-report and performance wisdom components, followed by necessary condition analysis and segmented regressions to examine whether the cognitive and personality variables under consideration here were necessary conditions for each wisdom component. We found that intelligence was necessary for the performance wisdom component whereas the Big-Five traits were necessary for the self-report wisdom component. This study is the first to demonstrate that high levels of wisdom are unlikely without some level of intelligence and adaptive personality traits.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it