Mathematics: A Place of Loving Kindness and Resilience-Building
Bibliographic record
Abstract
Places of mathematical learning are not always places of loving kindness. Instead, they are sometimes loci of undetected cultural violence (Galtung, 1969) and associated harm. We explore how Cousin’s (2015) interpretation of love in the context of early years relates to building mathematical resilience across the lifespan. Our interpretation of loving kindness in the context of older learners includes unconditional positive regard (Rogers, 1961) and the explicit building of this into the classroom milieu. Education is understood in this work in a broad sense, not only as a means of acquiring knowledge and skills, but also an arena for making connections and gaining a shared understanding about what it is to be human (Tagore, 1933). One of the tools found helpful in the practice of loving kindness, especially where learners have experienced significant prior harm, is the growth zone model (Lugalia, Johnston-Wilder, & Goodall, 2013), informed by the hand model of the brain (Siegel, 2010) and the relaxation response (Benson, 2000). With unconditional positive regard, and with such tools, learners may be empowered to become less avoidant and more engaged with mathematics. They may also acquire resilience, including coping skills, to on greater challenges, once perceived as dangerous. Loving kindness in mathematics is enabling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".