“Let’s Not Have the Perfect Be the Enemy of the Good”: Social Impact Bonds, Randomized Controlled Trials, and the Valuation of Social Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article uses the case of "social impact bonds" (SIBs) to explore the role of social science methods in new markets in "social investment." Pioneered in the UK in 2010, SIBs use private capital to fund social programs with governments paying returns for successful outcomes. Central to the SIB model is the question of evaluation and the method to be used in determining program outcomes and investor returns. In the United States, the randomized controlled trial (RCT) has been the dominant method. However, this has not been without controversy. Some SIB practitioners and investors have argued that, while this may be the perfect tool, the need to grow the SIB market demands a more pragmatic approach. Drawing from a three-year study of SIBs, and informed by Science and Technology Studies (STS)-inspired work on valuation and the social life of methods, the article explores RCTs as both a valuation technology central to SIB design and the object of a micropolitics of valuation which has impeded market growth. It is the relationship between, and the politics of, evaluation and valuation that is a key lesson of the SIB experiment and an important insight for future research on "social investment" and other settings where methods are constitutive of financial value.
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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 | 0.029 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it