Grasping the paradoxical nature of wisdom through unconscious integrative complexity
Bibliographic record
Abstract
There has been much progress in the scientific study of wisdom on both conceptual and empirical fronts in the past few decades. Despite all the progress being made, there are still gaps that can be filled to provide even more explanatory power and coherence. Although academic discourse on wisdom has included the ability to integrate issues in a complex manner, there is still room for improved theorizing on wisdom's integrative complexity. Since integrative complexity has both conscious and unconscious dimensions, including the latter in discussions on wisdom will add a valuable aspect to its conceptualization. This article will argue how unconscious integrative complexity is the variable in wisdom's conceptual equation that involves paradox, which is a well-known sign of wisdom. Explanations contrasting conscious integrative complexity and unconscious integrative complexity in reference to wisdom will be discussed. Then, the Archetypal Test of the Nine Elements will be proposed as a testing instrument to operationalize unconscious integrative complexity. After the conceptualization and operationalization are worked through, we will conclude with a couple examples to illustrate our reflections.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".