Making Visible and Acting on Issues of Racism and Racialization in School Mathematics
Bibliographic record
Abstract
Schools, as social systems, may knowingly or unintentionally perpetuate inequities through unchallenged oppressive systems. This paper focuses on mathematics as a subject area in school practices in which inequities seem to be considered normal. Issues of racism and racialization in the discipline of mathematics are predominantly lived through the practice of streaming where students are enrolled in courses of different levels of difficulty. Such practice denies marginalized groups of students the full benefit of rich learning experiences. These issues should be of concern for activists, advocates, and allies as well as individuals and groups who are systematically and directly affected. The purpose of this paper is to make visible issues of racism and racialization in school mathematics to a range of stakeholders that include: school administrators, teachers, students, parents, education advocates, academics, educational researchers, and politicians. The ultimate goal is that the knowledge gained through this call to action will contribute toward eliminating social injustice in all school systems, particularly as it relates to skin colour, country of origin, culture, language, customs, and religion.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".