Teaching As Meditative Inquiry: A Dialogical Exploration
Bibliographic record
Abstract
This is a conversational paper that explores an unconventional pedagogical approach—teaching as meditative inquiry—as developed by Ashwani Kumar. This pedagogical contribution is explored and expounded upon through a related research methodology called dialogical meditative inquiry (DMI). DMI emphasizes listening holistically, learning from silence, as well as having an open and vulnerable attitude to allow for a deeper engagement with self and other participants where inner thoughts and feelings may be expressed in meditative awareness. Through this dialogic approach, the authors explore the concept of meditative inquiry and the ideas of Jiddu Krishnamurti, as well as how these have informed Kumar’s professional practice as a teacher educator and scholar. Emergent themes from this dialogue include: 1) how Kumar’s concept of meditative inquiry began and developed; 2) the connection between holistic thinking and meditative inquiry; 3) differences in how “holistic” is conceptualized from Western and Eastern perspectives; 4) teacher education candidates’ perceptions of holistic education; and 5) examination of resistance toward self-inquiry and the instrumentalization of meditative approaches.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.043 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| 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".