Anti-Black Racism & Mathematics: Designs for Intentionally Fostering Courageous Conversations in a Knowledge Building Community
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, there has been growing attention in the learning sciences to address fundamental assumptions surrounding the nature of knowing and learning together, with renewed urgency to intentionally design for more equitable classroom practices.This study explores the implementation of a principles-based approach to designing an anti-racist mathematics classroom focused on fostering students' critical data literacy skills.Over the course of one semester, a grade 8 teacher engaged her students in critical conversations about carding in Toronto through sustained engagement with authoritative sources, real-world datasets, student-generated theories in Knowledge Building circles and Knowledge Forum.Qualitative analyses reveal the power of using analytic tools to restructure power dynamics in the classroom, as well as the critical role of idea diversity in helping students arrive at rise above theories of systemic oppression.Educational and moral implications of this work are discussed within the context of growing inequities in today's societies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it