Assessment as a Site of Anti/Oppressive Social Work Practice: Negotiating with Power and the De/Professionalisation of Social Work
Bibliographic record
Abstract
Abstract Social work practice starts with an effort to understand clients-in-context, a task which involves a process of assessment. Whilst social workers often assess clients-in-systems, rarely do they consider the social worker-in-systems as part of the client’s context. Guided by Foucault’s concepts of disciplinary and biopolitical power and related constructs of ‘Panopticism’ and ‘homo oeconomicus’, this article interrogates how the social worker’s observational gaze in assessment has become veiled in practice. Using a critical review method, the author examines how Foucault’s notion of a ‘faceless gaze’ has been increasingly intensified by the use of information technology in ‘common assessment’, thereby transforming the fundamentals of assessment from understanding the client for ‘care’ to ‘managing risk’ in neoliberal governance. This article historicises and politicises temporal discourses of social work assessment and illustrates how the worker’s embodied knowledge of assessment as a governing apparatus may solidify and/or endanger the social work profession. Locating assessment as a site of social in/justice, this critical review on the inevitable workings of power in assessment invites social workers to re-think the boundaries of de/professionalisation and to critically reflect on and re-imagine everyday institutional practices in social work assessment.
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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 | 0.091 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.164 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".