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 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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".