“Let’s Not Have the Perfect Be the Enemy of the Good”: Social Impact Bonds, Randomized Controlled Trials, and the Valuation of Social Programs
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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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 | 0.392 | 0.580 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".