Bibliographic record
Abstract
Prosocial behavior is crucial for tackling global challenges such as climate change, poverty, and conflict, yet people often prioritize personal benefits over the common good. A classic philosophical proposition is that prosocial behavior benefits from psychological wisdom - a concept characterized by cognitive and behavioral scientists by expression of intellectual humility, open-mindedness towards different ways in which events may unfold, as well as consideration and integration of diverse viewpoints. We investigate the relationship between these features of wisdom and prosocial behavior in an incentivized donation experiment, as well as self-reported real-world behaviors such as blood and charity donations across 13,500 households in nine European countries. Our findings reveal that greater expression of wisdom was systematically aligned with contributions to climate change mitigation, donating blood and money to charitable causes, compliance with rules and behaviors to contain the spread of the COVID-19 virus, voting in parliamentary elections, volunteering and being a member of an environmental group. These results were robust across experimental conditions varying vantage point (self-focused or other-focused), when examining wisdom in reflections specific to climate donation decisions, or reflections on one's personal life experiences, or when accounting for effect socioeconomic characteristics, personality, and values of prosocial behavior. Finally, the association was observed in each of the country samples, albeit with varying strengths.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".