Philosophical Counselling through Meditative Inquiry: Insights for Holistic and Contemplative Educators and Practitioners
Bibliographic record
Abstract
This paper explores how meditative inquiry can contribute to philosophical counselling, and how these two perspectives together can inform the work of holistic and contemplative educators and practitioners. After outlining the definition, goals, and practices related to meditative inquiry and pointing to its transformative potential, the author draws attention to how meditative inquiry and philosophical counselling share similar goals, such as engagement in authentic inner exploration of questions of deep existential significance, and highlights the benefits of processes related to meditative inquiry, such as developing attentive listening and observation, engaging in creative and aesthetic exploration, and connecting with nature. Rather than medicalizing and pathologizing personal and relational conflicts, meditative inquiry and philosophical counselling encourage us to delve deeper into them through critical thinking, self-reflection, and dialogue, and embrace authentic transformation. Given their emphasis on holistic understanding and transformation, the author believes that philosophical counselling and meditative inquiry can provide theoretical and practical support to holistic and contemplative educators and practitioners, including schoolteachers, university professors, mindfulness practitioners, and counsellors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".