Practical Strategies of Disruption for Dismantling White Supremacy in Ontario�s Education System
Bibliographic record
Abstract
This article examines the Ontario education system�s use of surface level practices intended to counter racism but that often fall short of creating authentic and sustainable change. Many schools across the province have focused their efforts on celebrating identity awareness days, weeks, and months, yet these initiatives largely reveal an apparent disconnect in understanding of the significant impact of white supremacy that is deeply embedded within the educational structural system. Stories from students, families, and community members reveal ongoing, often unintentional, acts of harm or violence against Indigenous, Black, racialized, and marginalized students, demonstrating a deep-seated failure to address systemic racism. The article explores the presence of harmful ideologies of white supremacy frameworks that permeate every facet of the education system, including the curriculum, communication, disciplinary actions, assessment, and institutional traditions and practices. By examining these issues, the article offers strategies for disrupting these structures, in order to dismantle white supremacy from the education system to support and empower Indigenous, Black, racialized, and marginalized students. By providing these practical examples rooted in a transnational solidarity lens, the article aims to empower educators to disrupt and dismantle white supremacy in the classroom and promote meaningful and long-lasting change.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.034 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".