When Shift Happens: Navigating Toward a Framework for Responsible Philanthropic Exits
Bibliographic record
Abstract
Philanthropy can be a powerful force for social change, with influence extending far beyond the funding period. When foundations decide to cease funding in a specific area, how they exit can significantly impact the field they are leaving in both the short- and long-term. A poorly executed exit risks blindsiding grant partners and communities, damaging key relationships, undermining progress, and potentially leaving the field worse off than it was found. In contrast, a responsible exit can help the work continue long after the foundation ceases its funding. What defines a responsible exit? In this article, learning leaders at three different philanthropies attempt to answer this question by drawing on literature, focus groups, interviews with foundation staff and nonprofit leaders, and their own experiences in philanthropy and the nonprofit sector. The resulting framework outlines seven core elements of a responsible exit to ensure that the ecosystem is as resilient and well-equipped as possible to continue the work when funders step away. This framework is shared with humility and the hope that others will improve upon it as the philanthropic sector advances its practice with a commitment to both equity and the perspectives of grant partners.
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.
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 | 0.117 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.103 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".