Addressing Sexism and Associated Gender Injustices Through Social Justice Mathematics Curricula
Bibliographic record
Abstract
Patriarchal and sexist discourses have limiting and negative influences on ideologies about sex and gender, personal and social identities, social practices, power relations, interpersonal relationships, and other aspects of being human, in Western democracies like Canada and the United States of America. Using different types of mathematics as interpretive tools has the potential to deepen adolescents' understanding of social injustices that are informed by patriarchal and sexist discourses. Two social justice mathematics (SJM) curricula for middle school students, and one SJM curriculum for Grade 9 students, were analyzed in this study using Michelle Lazar's (2005) principles for conducting feminist critical discourse analysis, as well as an analytical method that was adapted from James Gee's (2011) method for carrying out discourse analysis. The findings of this project give insight into what should be considered by SJM educators who would like to design SJM curricula that can specifically support students' development of a revolutionary feminist consciousness regarding sexism and associated gender injustices. Based on the findings common to all three SJM curricula, several implications related to SJM curriculum development, as well as research about it, emerged.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".