Bibliographic record
Abstract
In this article, we argue that virtues can emerge from contemplation which can lead us to attunement with the Dao thereby realizing our inner goodness and intrinsic traits. This requires us to persist in doing inner and outer work. Inner work involves meditation and reflective practices to awaken ourselves and others. Outer work involves engaging others and ourselves with loving-kindness, compassion, joy, and equanimity. The article discusses the transformative perspectives and practices in Daoist, Confucian, and Buddhist contemplative traditions that lead to the development of virtue and alignment with the Dao; further, it examines contemporary contemplative practices and new scientific discoveries, providing evidence that virtues of a leader can emerge from within. In other words, virtues can be cultivated, and there are rich traditions and methods in both Western and Eastern philosophies and religions that facilitate the emergence of virtues through contemplative and other religious practices. Character building and virtue training are linked to inner contemplative work, which we argue need to be integrated with cultivating vital life energy and doing good for the world. Character education and transformation are much more effective when supported by contemplative practices and inner work because changes come from deep within the heart. The integration of these two elevates our awareness of our interconnectivity with all and enhances our ability to serve the world lovingly and humbly, which are hallmarks of virtuous behaviors.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".