Integrate Social Justice Into the Mathematics Curriculum in Learning
Bibliographic record
Abstract
Although concerns for equity have become ever more central within mathematics education, there is still little consensus on how the term should be defined or how to effectively work towards equity in classroom learning. Equity initiatives that rely solely on arguments about achievement gaps can be dangerous, as they can perpetuate deficit notions of marginalized students and position dominant groups as the norm to which others should aspire. The project is analyzed using Marilyn Cochran-Smith’s six principles of pedagogy for teaching for social justice. In the study group, teachers were involved in designing research projects to honour their students’ cultural and community knowledge and to develop mathematics teaching with a social justice focus. We offer three examples of teaching mathematics for social justice in diverse classrooms, and consider the broader implications of inquiry projects such as these. While we and the teachers came together as a group because of a shared interest in using a social justice approach to mathematics teaching. We found that the study group process ended up emphasizing some aspects of teaching for social justice – in particular, bringing social justice issues explicitly into the curriculum, building on student interests and experiences, and working with families and community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".