Labelling Clients ‘Risky’: Social Work and the Neo-Liberal Welfare State
Bibliographic record
Abstract
The literature on neo-liberal transformations of the welfare state and forms of governance-inspired by Foucault's concept of governmentality-has much to offer theorizations of the role of social work in contemporary society. Rather than assuming a top/down analysis in which power is located within individuals or institutions, power is ‘not a matter of imposing a sovereign will, but instead a process of enlisting the cooperation of chains of actors who “translate” power from one locale to another’ (, p. 182). The profession of social work occupies an intermediary space, charged with ‘translating’ state power to individuals, families, groups and communities. In this paper, I use the experience of criminalized women to bring to life the theoretical understandings offered by neo-liberal analyses of the regulation of social marginality. I focus particularly on one key feature of neo-liberal governance-'risk thinking'-and examine the gendered nature of risk as a neo-liberal regulatory strategy across the penal-welfare complex. I conclude with a discussion of lessons learned from criminalized women about social work and risk 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.128 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".