Bibliographic record
Abstract
FIVE WelfarisationWelfarisation is another technology of government through which female illicit drug users are governed.It is the process that constructs individuals or groups as needing social support, or that constitutes them as unworthy of it.Governments themselves create welfarisation through the maintenance of structural inequalities, social and economic marginalisation and its 'management'.Certain 'needy' or 'at-risk' groups of individuals are targeted for welfarisation or 'soft policing' through formal and informal social control mechanisms (Worrall, 2001).Welfarisation is in principle, benevolent, and may involve the provision of support with social funds, housing, training, jobseeking or childcare.It may prove to be a lifeline for some individuals, but programmes of welfare have long been identified as having (darker) mechanisms of surveillance and social control embedded within them.This relates to Foucault's concept of the 'carceral continuum' and his view that regulatory techniques permeate 'a whole series of institutions ... well beyond the frontiers of criminal law' involving doctors, social workers and educators (1991 [1975], p 297).Drawing on Foucault's work, Cohen (1985, p 3) argues that liberal capitalist countries such as the UK, Canada and the US all have 'social control systems' embedded in their programmes of 'welfare' and ideologies of treatment.The idea that policies of welfare also operate as mechanisms of control and surveillance particularly over marginalised groups of individuals has been explored and developed by various writers across a range of disciplines and subjects (see Parton, 1991, on child protection; Carlen, 1988, on young women in care; Carrington, 1993, on juvenile girls; Phoenix, 1999, on sex workers).Interventions into the lives of women who use illicit drugs presented as policies and practices of welfare (concerned with their wellbeing) are often experienced as intrusive, coercive and punitive (see Chapter Seven,.Mechanisms of control and surveillance, including practices
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".