Thin-Slice Measurement of Wisdom
Bibliographic record
Abstract
Objective Measurement of Wisdom within a short period of time is vital for both the public interest (e.g., understanding a presidential election) and research (e.g., testing factors that facilitate wisdom development). A measurement of emotion associated with wisdom would be especially informative; therefore, a novel Thin-Slice measurement of wisdom was developed based on the Berlin Paradigm. For about 2 min, participants imagined the lens of a camera as the eyes of their friend/teacher whom they advised about a life dilemma. Verbal response and facial expression were both recorded by a camera: verbal responses were then rated on both the Berlin Wisdom criteria and newly developed Chinese wisdom criteria; facial expressions were analyzed by the software iMotion FACET module. Results showed acceptable inter-rater and inter-item reliability for this novel paradigm. Moreover, both wisdom ratings were not significantly correlated with Social desirability, and the Berlin wisdom rating was significantly negatively correlated with Neuroticism; feeling of surprise was significantly positively correlated with both wisdom criteria ratings. Our results provide the first evidence of this Thin-slice Wisdom Paradigm's reliability, its immunity to social desirability, and its validity for assessing candidates' wisdom within a short timeframe. Although still awaiting further development, this novel Paradigm contributes to an emerging Universal Wisdom Paradigm applicable across cultures.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".