Does Sadness Sell? The Use of Negative Emotions in Fundraising Appeals: Fundraising Strategies for For-profit and Nonprofit Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fundraising appeals frequently feature sad victims. This research postulates that the evaluation of fundraising appeals by consumers and their willingness to donate are contingent upon the congruence between organizational stereotypes (warm vs. competent) and the intensity of the negative emotion expressed in the appeal. Results show that when for-profit organizations employ negative emotional narratives, rather than non-emotional factual appeals, evaluations are less favorable (Experiment 1). Additionally, highly negative emotional appeals featuring multiple sad children designed to evoke compassion do not increase donations in for-profit fundraising campaigns (Experiment 2). These findings suggest that negative emotional appeals may backfire on for-profit organizations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it