Searching for Feminist Discourse in Social Justice Mathematics
Bibliographic record
Abstract
The purpose of this paper is to understand how the intended transformative potential of social justice mathematics (SJM) curricula about the social problems of sexism, patriarchy and associated gender injustices might be strengthened when the role of discourses is considered. Through feminist critical discourse analysis we examined the extent to which a middle school curriculum designed to highlight missing and murdered Indigenous women is a consciousness-raising text with respect to sexism and associated injustices. We found the lesson to be a strong example of a potentially transformative learning experience about gender injustices that partly stem from sexist discourses. However, we share the teacher’s view that the activity , like his other gender justice-oriented lessons, cannot capture all of the complexity of the issues connected to its central topic. Mathematics is an important tool for understanding social realities. Viewing the SJM curricula through a feminist discursive lens opens the door to further research that contrasts the approach in the lesson to what we see as a pervasive patriarchal discourse in schooling and society.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".