Practising life writing: teaching through vulnerability, discomfort, mindfulness, and compassion
Bibliographic record
Abstract
In this dissertation I engage in life writing and literary métissage (Chambers, Hasebe-Ludt, Leggo, & Sinner, 2012; Hasebe-Ludt, Chambers, & Leggo, 2009) to explore and exemplify mindful, aesthetic, and compassionate practices for working through moments of crisis (Kumashiro, 2010) in teaching and learning. The dissertation is designed as a four-strand braid and organized around the active verb “practising” to dig deep into the dynamic, and often difficult, processes of teaching and learning: (1) Practising Vulnerability; (2) Practising Discomfort; (3) Practising Mindfulness; and (4) Practising Compassion. Each strand is composed of different genres of life writing: theoretical and analytical introductions, letter writing, journal pieces, comics, photos, poetry, creative non-fiction, collages, scenes from a play, and an alphabet book. The multimodal life writing pieces are worked examples (Gee, 2010) of contemplative practices and pedagogical praxis. Life writing offers concrete ways to practise mindfulness, reflection, and reflexivity, which, in turn, invite a more awakened, critical, and compassionate stance as an educator. If teachers want to move beyond simply promoting the importance of reflective practice, wellbeing, self-actualization, and compassion to their students then we need to show more teachers (and teacher educators) the messy process of doing so themselves. Reading life writing is a starting point for teachers at all stages in their careers to imagine how they could, or already do, engage in similar processes and invite them to cultivate compassion and self-compassion as a grounding stance for their life projects as teachers, learners, and human beings. My autoethnographic teacher inquiry (Cochran-Smith & Lytle, 2009) was prompted when I encountered “troubling” (Kumashiro, 2009) tensions when first teaching about homophobia and transphobia to teacher education students at a faculty of education on the Canadian prairies. I began to explore the vulnerability and discomfort of this teaching moment from an experimental (Davies, 2011), multimodal (Kress & Street, 2006; Pahl & Roswell, 2006), critical literacy stance (Janks, 2010; Vasquez, Tate, & Harste, 2013). My inquiry shifted after a diagnosis of breast cancer, which became an opportunity for me to awaken to more mindful, empathetic, and compassionate ways of being, living, teaching, and researching.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".