The Power of Indirect Appeals in Peer‐to‐Peer Fundraising: Why “S/He” Can Raise More Money for Me Than “I” Can For Myself
Bibliographic record
Abstract
The proliferation of peer‐to‐peer fundraising platforms (e.g., GoFundMe, Rally, Fundly) poses conceptual and substantive challenges for behavior scientists and fundraisers. This article explores how fundraisers should craft their appeals to maximize their chance of success. Four field‐ and laboratory‐studies find that direct appeals (i.e., narratives written in the first person by the intended recipient) raise less money than otherwise‐identical indirect appeals (i.e., narratives written in the third person, seemingly by a third party on behalf of the intended recipient). The cause? Prospective donors ascribe lesser (greater) credibility to direct (indirect) appeals, which in turn curtails (increases) their giving. Since the narrative voice (direct vs. indirect) in which appeals are crafted is often discretionary (i.e., adjustable), our findings offer prescriptive guidelines for fundraisers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".