Renderings of Loving Kindness: Dialogic Engagements in the Mathematics Classroom
Bibliographic record
Abstract
I imagine mathematics to be a place of loving kindness and dialogue. In completing the above prompt for this Special Issue of JCACS, I invoke a fraught history of teaching and learning, drenched in contradictory feelings of love, hate, sadness and joy. Though pedagogically rigorous, many days in my classroom have been marked by tension, anxiety, and even cruelty, in the name of producing elegant, beautiful solutions to cleanly explicated problems. Determined to change these dynamics, I embarked on a recent journey with my high school mathematics students to cultivate a mindfulness practice of loving kindness by exploring new ways to engage in dialogue and open up possibilities for self-reflexivity. In this paper, I offer a rhetorical analysis (Felman, 1982) of my pen-and-ink graphical journal entries from past and present, alongside student drawings and reflection cards that were the result of new dialogic approaches in teaching mathematics. Through an emerging loving kindness pedagogy, this work begins to reveal the kinds of defences that fill the playground of psychic life unfolding in the mathematics classroom, as well as possibilities for future learning.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".