Centering Anti-Racism in Social Work Education: Integration of Critical Race Theory Across an MSW Curriculum
Bibliographic record
Abstract
Ranging from a multicultural approach to models of cultural sensitivity and cultural competency, social work education has historically avoided challenging the power of racism in shaping inequity in the United States. We argue that integrating critical race theory (CRT) in social work education decenters whiteness, counters color-evasive racism in education, and centers anti-racist ideas and practices. CRT provides social work educators with a framework that explicitly addresses race and racism while challenging social work students to self-reflect critically on their own experiences with privilege and oppression. Further, it enables social work students and practitioners to analyze race and other systems of oppression structurally. This manuscript offers an overview of how CRT is integrated across an MSW curriculum to better prepare social work students to engage in anti-racist social work practice. We describe specific examples of how CRT is infused into the curriculum in theory and practice courses. We conclude with an acknowledgment that CRT is not without limitations and call for more empirical research that assesses the effectiveness of CRT’s application to social work praxis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".