Charitable assets: social outcomes, financial values, and the new, nonprofit funding regime
Bibliographic record
Abstract
Amidst growing signs of inequality, poverty, and marginalization, there have been calls for a new approach to funding the social sector organizations tasked with addressing these challenges. Rather than paying for services, governments and philanthropists are being encouraged to fund programs based on their ‘outcomes.’ This paper explores this growing movement around ‘outcomes-based funding’ (OBF) suggesting that outcomes in this context embody a distinctly financial logic and reflect an effort to turn the work of charities and nonprofits into a type of pseudo asset. The paper teases out these dynamics and their implications based on one particular form of OBF, the social impact bond, a financial instrument which uses private capital to fund social programs and calculates returns based on program outcomes. While SIBs have struggled as a market, the operations underlying these projects and informing the production of outcomes as investable assets have been carried forward into non-SIB work informing flows of public and philanthropic capital and embodying the practices of the larger OBF ecosystem. As a window into the new, outcomes-based nonprofit funding regime, the paper offers a unique lens and set of critical tools for exploring the relationship between capital, the social sector, and poverty governance.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".