Social Impact Bonds: shifting the boundaries of citizenship
Bibliographic record
Abstract
Introduction One result of the reforms pursued by governments across the world to reduce public expenditure deficits since the 2008 financial crisis has been a growing interest in outsourcing the funding and delivery of welfare services. In the UK context, austerity measures and the demand for greater policy innovation have been strongly associated with the application of market incentives and business principles to social welfare provision. For example, the UK Cabinet Office's Green Paper Modernising Commissioning (Cabinet Office, 2010) reaffirmed the government’s commitment to extending payment by results (PbR) mechanisms across public services. The UK government has declared that ‘new forms of commissioning and contracting … improve both the outcomes derived from delivery of public services and the value for money achieved by public expenditure’ (Cabinet Office, 2013a). Social Impact Bonds (SIBs) are the most recent example of this policy trend. According to their supporters, ‘SIBs offer an answer to a question all policy makers are facing in these difficult fiscal times: How do we keep innovating and investing in promising new solutions when we can’t even afford to pay for everything we are currently doing?’ (Azemati, et al 2013, p 24). SIBs harness private investment to finance innovative welfare services, and the strength of the UK government's interest in them is testified to in its creation of a Centre for Social Impact Bonds within the Cabinet Office and the establishment of a £20 million Social Outcomes Fund designed to support the development of PbR methods and SIBs (Cabinet Office, 2013b). However, interest in SIBs is international – they are currently being considered or developed in the US, Canada, New Zealand, Australia, Columbia, India, Ireland and Israel in relation to a wide range of policy areas, including reducing offending and recidivism, tackling homelessness, employability and active labour market measures and provision of early years education (Robinson, 2012). The possibility of extending the SIBs model to create Development Impact Bonds to fund social and medical programmes in the developing world has also been proposed (Rosenberg, 2013). SIBs are certainly an interesting idea, but they are also a significant innovation in how social welfare services are funded and provided.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".