Exploring Mindfulness in Teaching-Learning Scholarship Through a Reflective Conversation
Bibliographic record
Abstract
We are three scholars in early childhood studies, child and youth care, and nursing in an urban university in Canada. In this chapter, we offer a conversation about our experiences as we explore the significance of conscious mindful practices within the context of our respective teaching–learning roles within academia. Drawing on a performative framework, this chapter offers a conversation shaped by reflections on our current collaborations in leading workshops on personal–professional well-being of our peers. Significant to the conversation is how, as actors in the classroom and research, we also engage with our students in mindful and creative performance to encourage self-care and well-being. Our reflective dialogue thus serves to inform not only our teaching–learning, but also our research undertakings.
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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 | 0.042 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.075 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".