In Social Studies, No One Can Hear You Scream: The Representation of Women and Gender in Ontario's Elementary Curriculum
Bibliographic record
Abstract
This research examines the portrayal of women in Ontario’s elementary social studies curriculum, the challenges faced by educators who wish to bring a more diverse approach to gender representation in their classrooms, and current strategies. The study comprises a qualitative analysis of the Ontario social studies curriculum and a selection of textbooks, along with a semi-structured interview with an Ontario educator. Data analysis revealed the following themes: the potential for diverse gender representation in the curriculum and resources; lack of priority, time/initiative and resources/knowledge as limitations preventing teachers from working toward gender parity; and suggestions for implementation based on the case study’s successful initiatives. While the Ontario curriculum provides the opportunity for a rich portrayal of gender and inquiry into women’s issues, the textbooks themselves often fall short of presenting teachers with usable material. As a result, the additional challenges mean many teachers are not moving beyond “default ” representation. Appropriate materials, professional development and pre-service training would go far in assisting teachers with increasing gender diversity in their classrooms. While the limited scope of this research invites further investigation, the diverse student makeup of Ontario schools necessitates a practical overhaul of the way teachers handle gender and women’s issues.
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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 | 0.002 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".