Othering in Canadian science textbooks: An analysis of visual, textual and discursive elements
Bibliographic record
Abstract
This study examined 17 science textbooks for grades 7-9 through a mixed-method approach that combined quantitative and qualitative analysis. We analyzed visuals, textual and discursive elements to explore how diversity is represented and whether these materials reinforce or challenge colonial narratives. Grounded in decolonial and post-colonial approaches, feminist science and technology studies and othering, the research applied Content Analysis and Critical Discourse Analysis (CDA) to identify issues of representation and colonial discourses. Our focus was on how social groups, places and historical times are positioned in the textbooks. Prior studies has emphasized that textbooks function as colonial artifacts, often embedding hidden messages that privilege Western science while marginalizing women, Indigenous peoples, and visible minorities. This research contributes to these debates by demonstrating how textbooks may act as mechanisms of exclusion, shaping perceptions of who produces legitimate knowledge. Findings show that across images, texts, and discourse, biased representations are reproduced. In images, visible minorities appear statistically overrepresented, yet intersections of gender, race, and role reveal that prominent scientists are almost exclusively White and male. Textual analysis confirms that most scientists highlighted are contemporary, but predominantly European and North American men. Discourse analysis further uncovers colonial logics, including binary oppositions that elevate Western science while erasing or subordinating alternative knowledges. By exposing these mechanisms of exclusion and authority, this study underscores how science textbooks reproduce social hierarchies. It advocates for efforts to decolonize science education and advance social justice by promoting inclusive and pluralistic understanding of knowledge construction.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".