Using Foundation Capital for Good: Opportunities in the Balance Sheet
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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