Let’s Give Together: Can Collaborative Giving Boost Generosity?
Bibliographic record
Abstract
A growing number of people donate to charity together with others, such as a spouse, friend, or stranger. Does giving to charity collectively with another person—called collaborative giving—promote generosity? Existing data offer unsatisfactory insight; most studies are correlational, present mixed findings, or examine other concepts. Yet, theory suggests that collaborative giving may increase generosity because giving with others could be intrinsically enjoyable. We conducted two well-powered, pre-registered experiments to test whether collaborative giving boosts generosity. In Experiment 1 ( N = 202; 101 dyads) and Experiment 2 ( N = 310; 155 dyads), pairs of unacquainted undergraduates earned money and were randomly assigned to donate collaboratively (Experiments 1–2), individually in each other’s presence (Experiments 1–2), or privately (Experiment 2). Across studies, we observed no condition differences on generosity. However, collaborative (vs. individual) giving predicted greater intrinsic enjoyment, which, in turn, predicted larger donations, suggesting a promising potential mechanism for future research and practice.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".