Leading and Learning From Inside Out: Insights from Meditative Inquiry
Bibliographic record
Abstract
This article explores our modern world and our role in shaping collective experiences. By our thoughts, choices and behaviours (our character), we influence our shared reality. Higher education has an imperative responsibility to engage students ethically and develop their character. I offer a teaching metaphor and case study to meaningfully develop students’ self-awareness of character via an undergraduate leadership course. I employed the ‘seed’ metaphor to illustrate how nurturing students’ self-awareness and agency mirrors the growth of a seed within a carefully cultivated ecosystem. Each contemplative practice (character development) intertwined with leadership content (competence and confidence), serves as essential nourishment, fostering interconnected growth, resilience, and transformation within students and in their broader interactions in our learning community. This curates the life-long development of an ethically impactful leadership style essential for handling complex challenges, leading others, and making sound decisions (Strum et al., 2017). The study highlights how a holistic pedagogical approach cultivates leadership qualities, inspires character growth, and encourages students to engage meaningfully with themselves, their peers, and their communities. Faculty who foster environments conducive to ethical leadership and societal well-being serve to better ourselves, our classrooms, and our communities.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".