A Commitment to Embodying Joy in the Service of Social Justice: Embodied Leadership when Exploring Difference
Bibliographic record
Abstract
This thesis explored the question, “How might I, a first-generation white Canadian settler of British and Irish ancestry, navigate difference through reflexive and embodied practices to more intentionally lead with joy in the service of social justice?” As a first-person action research inquiry applying an action learning research methodology, methods included reflexive experiential learning, structured and emergent journal practices, and semistructured dialogues. This study found somatic indicators supported researching which was complex, and embodied leadership in the service of social justice may experiment with a yes-and approach to shift from old to new behaviours. Findings highlighted my interwoven and oscillating learning through moments of joy, difference, and actions toward social justice. Findings included reembodying the whole person; reclaiming my more authentic, wild wisdom; acknowledging white fragility and still acting in allyship; and learning to embody joy. Recommendations for embodied leadership praxis and areas for further study arose from this study.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".