Math-a-POLKA: Mathematics as a Place of Loving Kindness and . . .
Bibliographic record
Abstract
In proposing this special issue, we sought to open a generative and healing conversation in the intersectionality of love, kindness, mathematics, curriculum and education, offering a call that invited the completion of the sentence, "I imagine/want mathematics to be a place of loving kindness and . . . " In their responses, contributors have highlighted the importance of communication in establishing loving kindness through patterns of caring responsiveness, acts of imagination and empathy, and conversations that matter. They envision a place of learning that values aesthetic and affective engagement in mathematical and pedagogical practices that promote capability and resilience among students. As guest editors, we each offer our responses to the prompt, exploring “kind-ness” as belonging and challenging readers to expand that further to the “kin’d-ness” of communal relations of multi-species flourishing, thus reimagining mathematics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".