A novel approach to legacy donations with long‐term benefits supported by numerical illustrations
Bibliographic record
Abstract
Abstract Philanthropic donors face challenges in matching the causes to which they donate, the time horizon—and thus impact—of their donations, and the charitable vehicles they choose for making contributions. Wealthier donors may elect to create their own foundations and customize their charitable support. Less wealthy donors have limited choices: they may contribute to a nonprofit's current operations or to existing nonprofit endowments. We present a novel approach for making charitable donations, blending aspects of each of these strategies. Our approach has potential long‐term financial benefits, allows donors to control their charitable donations in a convenient and easy‐to‐implement manner, can be established through an existing nonprofit organization, expands opportunities for more donors because it requires a smaller corpus contribution with lower management costs than creating a foundation, provides tax savings in the United States and other countries (e.g., the UK, Canada, and Australia) comparable to other planned giving vehicles, and may be implemented during one's lifetime using donor advised funds or as part of a legacy plan through the donor's estate documents, which is when the long‐term benefits accrue.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".