The emotional consequences of donation opportunities
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
Charities often circulate widespread donation appeals, but who is most likely to donate and how do appeals impact the well-being of individual donors and non-donors, as well as the entire group exposed to the campaign? Here, we investigate three factors that may influence donations (recent winnings, the presence of another person, and matched earnings) in addition to the changes in affect reported by individuals who donate in response to a charitable opportunity and those who do not. Critically, we also investigate the change in affect reported by the entire sample to measure the net impact of the donation opportunity. Results reveal that people winning more money donate a smaller percentage to charity, and the presence of another person does not influence giving. In addition, large donors experience hedonic boosts from giving, and the substantial fraction of large donors translates to a net positive influence on well-being for the entire sample.
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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 | 0.002 | 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 | 0.001 |
| 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 it