Cultivating Awareness, Reverence, and Autonomy in Students: Meditative Inquiry as a Catalyst to Holistic Learning and Living
Bibliographic record
Abstract
This conversational paper between Ashwani Kumar and Shane Theunissen explores how meditative inquiry in teaching and learning can foster reverence for nature, life, and learning. Through an organic and reflective conversation rooted in Dialogical Meditative Inquiry (DMI), the authors offer a holistic exploration of the intersections between personal transformation, environmental awareness, and holistic education. The authors emphasize that meditative inquiry challenges predetermined educational outcomes and encourages a profound, personal transformation that can lead to social change. Authors discuss how meditative inquiry can facilitate learning relationships that promote student autonomy and awareness, and how it can instill reverence for life. The paper considers how awe, wonder, and reverence can shift educational paradigms from mechanistic models toward contemplative, relational approaches informed by Indigenous and meditative perspectives. The conversation also highlights the strong connection between meditative inquiry and Indigenous ways of knowing, both of which are rooted in a deep reverence for nature and a harmonious relationship with the natural world. The paper promotes a contemplative and holistic approach to education and living, suggesting that personal transformation through meditative inquiry can contribute to a more respectful and interconnected relationship with oneself, others, and the environment. By challenging dominant narratives in education and promoting meditative and emergent dialogue, the authors advocate for education as a regenerative and transformative practice grounded in deep listening, interconnectedness, and awareness. Keywords: meditative inquiry, reverence, dialogical meditative inquiry, holistic education, nature, Indigenous philosophy
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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 | 0.005 | 0.018 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".