Cracking Open the Literary Canon: Disrupting English Curricula Through Relational Reading
Bibliographic record
Abstract
Emerging as a form of pedagogical action research, my study works to develop a reading practice that confronts, rather than ignores or misrepresents, the complex histories of colonialism embedded in the literature that is read in high school English classrooms. Following the research documenting a sustained, disproportionate reliance on the Eurocentric literary canon in English classrooms across Canada, I argue that the way this literature is taught generally leaves students with an ahistorical and narrow worldview that fails to recognize the colonial violence embedded in these narratives. To disrupt this legacy, my project employs Critical Discourse Analysis and Métissage to develop a reading practice I denote as “relational reading.” In developing this praxis, I engage three texts—Shakespeare’s The Tempest, Gale’s Angélique, and Dimaline’s The Marrow Thieves—in intertextual conversation. By tracing the sustained impact of these systems of power across texts, I suggest that this study might help re-envision literature education as a means for confronting these violent histories, while helping students imagine how we can work together towards forging anticolonial futures rooted in solidarity, accountability, and reciprocity.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.060 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".