Welfare Law, Welfare Fraud, and the Moral Regulation of the ‘Never Deserving’ Poor
Bibliographic record
Abstract
The dismantling and restructuring of Keynesian social security programmes have impacted disproportionately on women, especially lone parent mothers, and shifted public discourse and social images from welfare fraud to welfare as fraud, thereby linking poverty, welfare and crime. This article analyzes the current, inordinate focus on ‘welfare cheats’. The criminalization of poverty raises theoretical and empirical questions related to regulation, control, and the relationship between them at particular historical moments. Moral regulation scholars working within post-structuralist and post-modern frameworks have developed an influential approach to these issues; however, we situate ourselves in a different stream of critical socio-legal studies that takes as its point of departure the efficacy, contradictions and inherently social nature of law in a given social formation. With reference to the historical treatment of poor women on welfare, we develop three themes in our critical review of the moral regulation concept: the conceptualization of welfare and welfare law, as illustrated by welfare fraud; the relationship between social and moral with respect to the role of law; and changing forms of the relationship between state and non-state institutions and agencies. We conclude with comments on the utility of a ‘materialist’ concept of moral regulation for feminist theorizing.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".