A Foundation’s Theory of Philanthropy: What It Is, What It Provides, How to Do It – With 2024 Prologue
Bibliographic record
Abstract
Editor’s Note: This article, first published in print and online in 2015, has been republished by The Foundation Review with minor updates. This article argues that philanthropic endeavors should be undergirded by a theory of philanthropy. Articulating a theory of philanthropy is a way for a foundation to make explicit what is often only implicit, thereby enabling internal and external actors to pose and resolve significant questions, understand and play important roles more fully and effectively, and improve performance by enhancing alignment across complex systems. A theory of philanthropy articulates how and why a foundation will use its resources to achieve its mission and vision. The theory-of-philanthropy approach is designed to help foundations align their strategies, governance, operating and accountability procedures, and grantmaking profile and policies with their resources and mission. Some 30 elements that can feed into a comprehensive theory of philanthropy represent a customizable tool for exploring the issues foundations face. A foundation can use the tool to gather data and perspectives about specific aspects of its heritage and approach; what is learned in addressing the elements can then be synthesized into a succinct and coherent theory of philanthropy.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".