Simulating the Other in Social Work Pedagogy: Pathologising the Oppressed through Neoliberal/Colonial Practice Teaching
Bibliographic record
Abstract
Abstract The use of simulated service users or ‘clients’ in social work education has lacked critical analysis in research and teaching. What is often overlooked are attentions to how constituting essentialised clients and simulating them advances historically entrenched forms of injustice in social work education and practice. This gap in research and literature in social work education should signal to the field an issue with the pervasiveness of the hegemonies of biomedical disciplinary knowledge in social work. This gap should also highlight the need to ask how, why and to what end is critical analysis an ongoing omission from a pedagogical practice that is proclaimed as very central and essential to social work practice skills education. This article explores some of these gaps in the literature within the social, historical, and political contexts that shape how neoliberalism/colonialism influence the perpetuation of these omissions in social work education. Specifically, this article explores how the use of client simulation in social work works to reproduce ideas of an innocent professional self and a pathological Other, while ejecting or separating social and political analyses of social problems, marginalising and obscuring critical perspectives, and forgoing attentions to social justice and epistemological nuances within social 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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.108 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".