If Not Now, When? A Call to End Social Work’s Tolerance of White Supremacy in the Academy
Bibliographic record
Abstract
Despite ethical responsibilities to dismantle systems of oppression, White supremacy ideologies and practices are still inundated in social work academe to the detriment of Black, Indigenous, Latino, and Persons of Color (BILPOC) communities and faculty dedicated to teaching the next generation of critical scholars, activists, and clinicians. Four themes are introduced to exemplify how the academy remains overpowered by the need to sustain the status quo of White power. In the first theme, social work’s long-standing history of omitting BILPOC experiences in curricula is discussed. The second theme characterizes social work’s legacy of omission via inaction to address unjust governmental practices at the U.S. Southern border, thereby perpetuating the cycle of White power. Cementing these positions, we shift the discussion to the inherent pressures within the academy that prizes productivity above all else, perpetuating the culture of White supremacy. In turn, spaces to engage in creative thinking and teaching to dismantle systems of oppressions are limited. Lastly, we discuss the increasing pressure to produce “eurocentric” rigorous scientific knowledge takes precedence at a time when we must place equity and fairness on equal footing. For each of these four themes, we offer suggestions for how to create spaces for racial reconciliation, healing, and equality.
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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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.039 | 0.098 |
| Scholarly communication | 0.035 | 0.031 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".