To pause, to notice, to wonder: living love, teaching in relation
Bibliographic record
Abstract
This thesis is an autobiographical narrative inquiry into my everyday stories from years of teaching and learning in Kindergarten in an urban elementary school in western Canada. These are stories that pulled at me to pause, to notice, and to wonder at the tensions within—at the embodied wisdom, the uncertainties, and then the new emergent and generative wonderings that they surface. The research puzzle that emerged from these stories is: Who am I and who might we become if we centre our relationality, if we live love? What does it mean within the school system to be present to and to live in alignment with relationality and love? Who might we become as we live into such possibility? There are three main threads in the inquiry that carry relational possibility: a thread of practising presence and attending to embodied wisdom of self, a thread of a needs-orientation that serves relationships, and a thread of equity and how systems are both shaped by and shape relationships. I hold these stories in conversation with others who bring a relational and love-oriented focus, such as Dwayne Donald, bell hooks, and Marshall Rosenberg; and who contribute to practising presence and embodiment—such as Maxine Greene, Prentis Hemphill, Oren Ergas, and Thupten Jinpa. The voices of all of the thinkers/writers/speakers/teachers/practitioners included in this thesis offer expanding possibilities and meaning potentials when their contributions are held in conversation together with my stories.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.041 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.006 |
| 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".