Mapping an Integrative Critical Race and Anti-Colonial Theoretical Framework in Social Work Practice
Bibliographic record
Abstract
The social inequities highlighted by the racial injustice protests of 2020 and the COVID-19 pandemic challenge the social work profession to respond to the past and present social consequences that disproportionately impact Black, Indigenous, and People of Color (BIPOC). We argue that social work's commitment to social justice has not taken up an explicit anti-racism mission to eradicate white supremacy, racism, and coloniality in the profession. We further argue that although social service agencies often include a commitment to cultural competence/humility, practices continue to be rooted in color-blind approaches to service and treatment. Social work's failure to address racism poses challenges for those from racialized backgrounds experiencing psychological distress due to racism and other inequities. Building upon the theoretical foundations of Critical Race Theory (CRT) and Anti-Colonialism, we provide a conceptual framework for practice and service delivery with BIPOC clients through social work praxis. This conceptual framework offers three overarching directives that include integrated critical race and anti-colonial theoretical concepts for social work practice and service delivery. We discuss the implications for application of this conceptual framework in practice and service delivery.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.094 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".