Designing a Game of Resistance: An Approach to Cultural Misrepresentation in Educational Visuals
Bibliographic record
Abstract
This thesis explores the pervasive issue of cultural misrepresentation within Ontario’s elementary school curriculum, with a specific focus on the visual content found in educational materials for grades 4 to 6. Through a qualitative content analysis of commercial and Ministry-endorsed teaching resources, this study reveals how visual narratives often reinforce Eurocentric ideologies, marginalize racialized identities, and offer reductive portrayals of culture under the guise of multicultural inclusion. Rather than fostering meaningful cross-cultural understanding, these representations tend to prioritize aesthetic cohesion, palatability, and marketability, ultimately compromising cultural accuracy and complexity. Guided by Semiotics, Critical Design Theory, and Decolonial Design Theory, this research interrogates the underlying pedagogical and design assumptions embedded in these visual tools. It argues that the erasure or tokenization of non-dominant cultures is not incidental but structurally ingrained, reflecting larger systemic inequities. In response to these findings, the thesis presents a speculative design intervention—an interactive, critical experience that visualizes and challenges cultural misrepresentation in educational media. This intervention aims not to offer solutions, but to provoke reflection, dialogue, and discomfort. Ultimately, the project advocates for a decolonial design approach that centers cultural authenticity, and amplifies marginalized voices to challenge dominant narratives and reimagine how culture is visually represented.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".