Meditative Inquiry and Indigenizing the Curriculum: A Complicated Conversation
Bibliographic record
Abstract
In this presentation, we will explore two questions: In what ways can meditative inquiry contribute to the process of Indigenizing the curriculum? What are some key philosophical and educational connections between meditative inquiry and Indigenous spiritual philosophy? This presentation has emerged from a conversational research project which employed Dialogical Meditative Inquiry (DMI) as a research methodology. Through their meditative and dialogical engagement, which was guided by their self-inquiry, deep questioning, academic knowledge, and practical experiences, the authors of this paper reached the following conclusions: 1) contemporary educational institutions over emphasize cognitive learning at the expense of holistic learning, which hinders the full development of children (Author 1,
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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 | 0.032 | 0.031 |
| 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 | 0.073 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".