Culturally Relevant Pedagogy – A Diffusion Model for District-Wide Change to Address Systemic Racism
Bibliographic record
Abstract
Culturally relevant pedagogy (CRP) has been implemented in classrooms and schools across Canada and the United States to address the inequity that has caused an academic achievement gap between Black and Indigenous students and those students who self-identify as White. The purpose of this paper, which draws upon a larger instrumental case study that investigated CRP as a district-wide change, is to demonstrate an effective model for sustainable, deep-level educational change to address systemic racism through CRP. The primary research question from the larger study was: How do people with different roles throughout the hierarchy of the school district make sense of CRP? In this paper, I highlight two of the key findings from the larger study. First, in order for CRP as a district-wide reform mandate to be implemented effectively, the steps of the reform must be diffused throughout the district rather than decreed from the top of the hierarchal chain of a typical public school system. Second, in order for change that impacts an entire school system to occur, there must be a mechanism for deep learning prior to and during the implementation stage for members of the district. Keywords: culturally relevant pedagogy, second-order change, decolonizing, sensemaking, university-school partnerships
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".