Anti-Racist Social Work Education: “Ready or Not, Here I Come, You Can’t Hide...”
Bibliographic record
Abstract
The 2022 Educational Policy and Accreditation Standards (EPAS) from the Council on Social Work Education (CSWE) definitively identifies anti-racism as a necessary component of social work education. This change supports an effort to ensure that coming generations of social workers are more than culturally competent, but rather actively anti-racist in their practice across the micro, mezzo, and macro spectrum. While some social work programs have already embraced anti-racist education, many still have significant work to do. The fact remains that every accredited school will be required to make this shift to stay in compliance with CSWE accreditation once the newly ratified EPAS comes into effect. Although changes are expected of social work schools/programs, guidance on how to make such changes has been scarce. This paper provides an overview of what is meant by anti-racist social work education and why it is important, inclusive of emphasizing the difference between rhetoric and praxis. Based on a narrative review of the literature related to social work schools/programs in the U.S. and Canada that began incorporating anti-racism prior to EPAS 2022, suggestions for encouraging strategies within both the implicit and explicit curricula that align with anti-racist social work education are offered.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".