Designing Markets Together: Nascent Financial Engineering for Social Innovation
Bibliographic record
Abstract
Social innovations create social value, but are severely underfunded. Globally, new public-private initiatives seek to build special financial infrastructures for social innovation. We study these initiatives under the lens of nascent markets and as a new type of financial engineering, which refers to purposeful, public-private efforts of creating new market infrastructures that support social value. We develop a typology of how these efforts pursue modest adjustments or more radical alternatives to capitalism. We also specify the inherent partnership and governance challenges, and how this affects the functioning of markets. First, financing social innovation introduces impact in addition to return and risk as a key criterion for governing markets. Second, there are positive and negative consequences of state intervention in promoting alternative models of capitalism. Third, shaping the social relations and institutional structure of markets in parallel can help unplug Ayn Rand’s famous Atlas, who holds capitalism in a deadlock.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".