Mapping the Coercive Turn: Universal Credit, Social Crisis, and the Politics of Welfare in Austerity Britain, 2010–2019
Bibliographic record
Abstract
This article examines the politics of welfare in Britain from 2010 to 2019. Drawing on Gramscian literature, the first section outlines an original framework of the ‘divide-and-rule’ politics of welfare during the 1980s and 1990s in the United Kingdom. The second section examines the return of welfare restructuring in Britain following the 2008 global financial crisis, focusing on Universal Credit. It contends that a significant escalation of coercive social policies within the social security system undermined previous social antagonisms underpinning the political coalitions of neoliberal welfare reform. Alongside deepening economic stagnation and dislocation exacerbated by austerity after 2010, it argues that this coercive turn intensified an unfolding crisis of legitimacy. The third section examines the politics of welfare amid an unfolding social crisis in Britain. It argues that despite burgeoning socio-political discontent and the emergence of the counter-hegemonic project of Corbynism, 2016–2019 was characterised by an interregnum. With the defeat of Corbynism amid protracted Brexit negotiations, this included a period of political impasse in which popular support for welfare reform, austerity and neoliberalism were in decline, but without an attendant shift in the balance of political forces to advance an alternative hegemonic project. As a result, a deepening social crisis continued to unfold.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".