Reversing the legacies: asset-based discourses for racialized social workers
Bibliographic record
Abstract
Social work has a lengthy history of ignoring and invalidating the assets that racialized practitioners bring to their work. This article seeks to shift the discourse in social work away from invisibility and diminishment toward visibility, positive valuing, and asset-rich perspectives. The rationale is rooted in original research, theory, and experience. We close the article with concrete recommendations for change. Research conducted in [state], USA, partnered with four culturally specific organizations and the university research team. An in-person Delphi study identified a set of 25 assets that these organizations agreed were pronounced within their workforce, and that contributed to the wellbeing of racialized clients. Defined as “staffing assets,” these experiences were then affirmed by a sample of 505 clients. To help organize the 25 assets into more standard constructs, we identify six domains in which to embed the assets: culturally grounded worldview and beliefs, effective advocate, invested in long-term community wellbeing, demonstrated respect and recognition, organic problem solver, and relationally focused. We then theorize these assets, drawing from previously published work. Closing with recommendations for improvements in the field, we prioritize workforce diversification, social work education reprioritizing, supervision improvements, building inclusive workplaces, infusing equity into research, and a shifted discourse within social work that foregrounds practitioners of color as holding essential assets for the wellbeing of racialized clients and communities. This contribution is a mere opening – as a field, we must dedicate scholarship and practice to building out practice frameworks that include social workers of color.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".