Which Features of Prosocial Spending Recollections Predict Post-Recall Happiness? A Pre-registered Investigation
Bibliographic record
Abstract
People frequently spend money on others and research shows that such prosocial spending often promotes the benefactor’s happiness, even sometimes when reflecting upon past prosocial purchases. But on whom and what do people generally spend their money? And what features of prosocial spending memories are associated with greater post-recall happiness? In a pre-registered examination, human coders and a text analysis software coded over 2,500 prosocial spending recollections for information regarding the target, content, and presence of five theoretically motivated dimensions: affiliation, volition, impact, authenticity, and level of detail. Exploratory analyses revealed that people often recalled buying gifts or food and typically spent money on significant others, friends, or children. Consistent with the pre-registered hypotheses, higher levels of volition and impact were associated with greater post-recall happiness (rs: .05 – .07), controlling for pre-recall happiness. However, in contrast to the pre-registered hypotheses, affiliation, authenticity, and level of detail did not predict greater happiness. These findings illuminate some key characteristics of prosocial purchases and the most rewarding features of people’s prosocial spending recollections.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".