Bibliographic record
Abstract
Interest in the topic of wisdom-focused education has so far not resulted in empirically validated programs for teaching wisdom. To start filling this void, we explore the emerging empirical evidence concerning the fundamental elements required for understanding how one can foster wisdom, with a particular focus on wise reasoning. We define wise reasoning through a combination of intellectual humility, recognition of world in flux/change, open-mindedness to diverse viewpoints, and search for compromise/integration of diverse perspectives. In this article, we review evidence concerning how wise reasoning can be facilitated through experiences, teaching materials, environments and cognitive strategies. We also focus on educators, reviewing emerging evidence on how the process of explaining and guiding others impacts one’s wisdom. We conclude by discussing the development of wisdom-focused education, proposing that greater attention to the situational demands and the variability in wisdom-related characteristics across social contexts should play a critical role in its development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".