Enacting a Black Excellence and Antiracism Curriculum in Ontario Education
Bibliographic record
Abstract
Given the ongoing persistence of anti-Black racism in Ontario education, I enact a curriculum of Black Excellence and antiracism. In partnership with the Ottawa Carleton District School Board and propelled by calls to action from The Ministry of Education and Black advocacy organization, I ask how The Sankofa Centre of Black Excellence course and program may address these systems of racism. I draw on Critical Race Theory as both a theoretical framework and overarching methodology of analysis for my thesis. In the first of three articles within this thesis I begin by framing my understanding of antiracism with an overview of the possibilities and limitation of Culturally Relevant and Responsive Pedagogy in Ontario public schooling contexts. In the second article, I draw on the literature and method of Critical Race Currere to understand antiracism and Black excellence in relation to teaching the Sankofa course. In the third article, I draw on a social action curriculum project research methodology to analyze and synthesize the course curriculum-as-planned and -lived. Finally, I suggest that the continued engagement with Aoki’s (1993) concept of a curriculum-as-lived serves as a departing point for engaging with broader conversations surrounding Black excellence and antiracism curriculum in the Ontario educational system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".