Lessons Learned from the CSWE Task Force to Advance Anti-Racism in the Social Work Education Policy and Accreditation Standards:
Bibliographic record
Abstract
On May 25, 2020, Mr. George Floyd, a Black man, was murdered by Derek Chauvin, a White police officer in Minneapolis, Minnesota. In response to this disgusting display of police brutality, thousands of people all over the world began protesting Mr. Floyd’s killing. The Capital of the Confederacy, Richmond, Virginia, became one of many flashpoints for the public’s rage against white supremacy and systemic racism as thousands of people flooded the historic Monument District to topple, dismantle and re-frame Confederate monuments with protest slogans. Policing in the United States is rooted in the historical memory of enslavement, the unrestrained and authorized misuse of power by law enforcement, and conflicting values of discourse community. Protestors employed historical memory, which includes resistance, tolerance and strength in the face of tremendously difficult circumstances (Corredor, Wills-Obregon, Asensio-Brouard, 2018, 184). This groundswell of protests merged with those that sprang up for Ahmaud Arbery, Breonna Taylor and others slain by police violence, producing a demand for racial justice that could not be stymied. Racial disparate treatment is embedded in police brutality and in all societal institutions. This movement calls into question social justice accountability within social work education, practice, and policy. Have the protests been enough? Will the profession of social work address its own complicity in maintaining racism? To advance anti-racist social work education, the CSWE Task Force for Advance Anti-Racism was conceptualized in summer 2020 to center anti-racism pedagogies and anti-racist learning environments. Several diverse social work leaders, educators, researchers, community organizers, and students came together to explore how the profession should be re-imagined as a profession that advances anti-racism and the decentering of whiteness. The task force members met to develop, discuss, and refine recommendations for CSWE on Education Policy and Accreditation (EPA). Employing content analysis, the authors identified major themes that emanated from the work of the Task Force. Content themes include how racism, white supremacy and ethnocracy underscores social work as an applied social science that maintains information structures, paradigms, theories, and practices ensconced in academia. The praxis recommendations of the task force include adapting theoretical frameworks for anti-racist social work education; incorporating anti-racism and critical theories, such as Critical Race Theory; updating social work competencies; promoting equitable approaches to hiring and retaining BIPOC (Black, Indigenous and People of Color) faculty in different positions; and, creating a new anti-racism commission to continue anti-racism work.
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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 | 0.055 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.018 | 0.033 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".