Using Foundation Capital for Good: Opportunities in the Balance Sheet
Bibliographic record
Abstract
Foundations increasingly use their full balance sheets to unlock more of their capital for good. They look beyond conventional grantmaking to pursue their charitable purposes in many ways that exemplify innovative, full-balance sheet approaches: investing in nonprofit and for-profit companies that offer clear social and financial returns; investing their corpus in companies whose products and services align with their missions; using social bonds to inject new resources into their programs; offering guarantees to help grantees manage risk; and avoiding companies whose practices run counter to their grantees’ efforts. This article looks at the structures, pathways, and tools for foundations wanting to use all their assets and strategies to enhance their positive impact, describes the context in which these efforts are occurring, and provides the landscape of actors and leaders. It also notes countervailing arguments to foundations using their balance sheet or grant dollars for anything but awarding grants mainly focused on opportunity costs and net social impact. In addressing some legitimate concerns, this article offers considerations and suggestions that may help foundations identify and evaluate their investment options. Amid the rapid evolution of impact investing, much remains to be done; there are gaps to fill and value to be created. This article concludes with a discussion of key opportunities and challenges for philanthropic foundations and all investors wanting to ensure a sustainable planet and the well-being of all people.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".