Marketing Bequest Club Membership: An Exploratory Study of Legacy Pledgers
Bibliographic record
Abstract
Although bequest income accounts for 9% of overall giving in the United States many nonprofits continue to focus their solicitation efforts on the very wealthy, ignoring the bulk of the fund-raising database. In this study the authors work with three large nonprofits operating bequest societies and using direct marketing to solicit gifts from across their fund-raising database. They compare the profiles of their bequest pledgers with nonpledgers to determine whether individuals willing to offer a bequest may be demographically or attitudinally distinct. Developing a discriminant function they correctly classify 77.1% of 624 respondents to a postal survey of 3,000 donors and/or pledgers. Legacy pledgers appear significantly more likely to be seeking a means of reciprocation, are more concerned that the organization be performing well, and are more concerned with the quality of communications they receive. The fund-raising implications of their analysis are explored.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".