Examining Systemic Racism in K-12 Education through a Decolonizing, Anti-Racist and Human Rights Lens
Bibliographic record
Abstract
This paper seeks to explore how human rights, decolonizing and anti-racist education converge in combatting systemic racism, bias and discrimination in K-12 schooling and education. Colonization is a major part of our country’s history. Because colonization is present in day-to-day attitudes, actions, systems and institutions, not addressing it hinders the ability to make change and further perpetuates marginalization, which then becomes normalized. Understanding human rights is important because acknowledging and respecting one another is fundamental. Human rights are a set of principles concerned with equality, fairness, dignity and respect. Key elements of human rights are freedom, choice, power and voice. I do not propose to embed human rights as a standalone framework, but rather, to align human rights principles with ongoing decolonizing and anti-racist work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".