Dis/counting of women : a critical feminist analysis of two secondary social studies textbooks
Bibliographic record
Abstract
Two secondary social studies textbooks, Canada: A Nation Unfolding, and Canada Today were analyzed with regard to the inclusion of the lives, experiences, perspectives and contributions of females throughout history and today. Drawing on the existing literature,-a framework of analysis was created comprised of four categories: 1) language; 2) visual representation; 3) positioning and; 4) critical analysis of content. Each of these categories was further broken into a series of related subcategories in order to examine in depth and detail, the portrayal of women in these two textbooks. Each book was carefully read and then analyzed for instances of gender bias as informed by the analytical framework. Neither of the books was. free from gender bias. Although the authors of the textbooks are careful to employ gender inclusive language, language used to describe women's lives, experiences and contributions is problematic. It often denies them agency and categorizes them as members of nameless, faceless collectives. Visually, women in these two textbooks are under represented, and the manner of the representation is problematic, particularly when attention is given to traditional and non-traditional roles (for both men and women). Frequently, information about women is included outside of the main text, reinforcing their historical marginalization. Finally, the textbooks were found to be neither fair or equitable with regard to women's historical contributions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".