A Rationale for an Anti-Racist Entry Point to Anti-Oppressive Social Work in Mental Health Services
Bibliographic record
Abstract
Anti-oppressive social work must address equity and social justice issues across a wide range of social difference. Practitioners and theorists often struggle to negotiate the multiple forms of oppression that operate simultaneously in a given practice context. This paper attempts to engage with that challenge by building a rationale for anti-oppressive action in the mental health care system to be focused on anti-racist change. It demonstrates that an achievable goal for social work is to be able to articulate and substantiate claims that systemic oppression affects service delivery, and has particular consequences for specific populations.
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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 | 0.054 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.025 | 0.123 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.031 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 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".