Bibliographic record
Abstract
Introduction Universal Credit was introduced to reorient the social security system around paid work. It aims to increase entry and progression in paid work, in part through intensifying and expanding conditionality. One means of increasing conditionality was to introduce a new conditionality regime for lead carers of dependent children. Numerically, women are disproportionately affected by this new regime. This chapter outlines the history, aims, design and delivery of Universal Credit. It then gives a brief history of conditionality in the UK before detailing the Universal Credit conditionality regime for lead carers. The chapter ends by presenting gender concerns that have been raised in relation to this new conditionality regime. History of Universal Credit Universal Credit has its origins in the Centre for Social Justice (CSJ), a think tank founded by Iain Duncan Smith in 2004, aimed at addressing the root causes of poverty (Haddon, 2012; Duncan Smith, 2017). Following an investigation into five perceived causes of poverty (family breakdown, educational failure, worklessness and economic dependence, addictions, and indebtedness), the CSJ started looking at options for simplifying the benefits system (Haddon, 2012). The CSJ then undertook detailed modelling of a single benefits system and, in further work with David Freud, devised Universal Credit. The opportunity to implement Universal Credit arose when Duncan Smith was appointed Secretary of State for Work and Pensions following the 2010 election. In 2010, the Department for Work and Pensions (DWP) published a Green Paper entitled ‘21st century welfare’ (DWP, 2010a), which outlined five options for simplifying the benefits system; however, it was clear that Duncan Smith and the DWP favoured Universal Credit (Sainsbury, 2010; Timmins, 2016). Universal Credit was then incorporated into the October 2010 Spending Review. In November of the same year, the DWP published ‘Universal Credit: welfare that works’ (DWP, 2010c), a White Paper which described the new benefit. The Welfare Reform Act 2012 outlined the framework for Universal Credit, and The Universal Credit Regulations 2013 provided the detailed policy for this new benefit. Amendments to the Universal Credit legislation were passed in the Welfare Reform and Work Act 2016. Key changes included increasing the conditionality within Universal Credit by lowering the thresholds (the age of the youngest child) at which lead carers have to carry out work preparation and job search requirements.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 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 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".