Dismantling Racism in Schools through Anti-Oppressive Frameworks: The Pivotal Role of Leadership in Achieving Racial Equity
Bibliographic record
Abstract
This paper explores how human rights, decolonization, and anti-racist education converge in combatting systemic racism, bias, and discrimination in K-8 schooling. The goal is not to embed human rights as a standalone framework, but to align human rights principles with ongoing decolonizing and anti-racist work. Educational institutions and school leaders have a moral, ethical, and legal responsibility to those they serve and lead. The onus must be placed on educational leaders to first, examine their own racial location and identity; second, be aware of their power and privilege, and; third, understand how this power, privilege, and bias shapes and impacts attitudes, beliefs, and decision-making. Without a fundamental understanding of one’s biases and knowledge gaps, leaders cannot adequately identify and eliminate racism, racial discrimination and inequities in schools. To move from theory to practice, this paper concludes with tangible strategies and tools for leaders to begin dialogues and processes for change. This paper is based on a theoretical research plan developed for the York University Graduate Students in Education Conference. In the future, this conceptual paper will inform the development of a research project, at which stage, the methodology will be solified, the theoretical frameworks more firmly grounded, and implications for leadership policy and practice discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".