Childhood predictors of charitable giving and helping across 22 countries in the Global Flourishing Study
Bibliographic record
Abstract
While prior work documents the individual and societal benefits of prosocial behaviors, less is known about how childhood experiences shape prosociality in adulthood. Using data from the Global Flourishing Study, a diverse and international sample of 202,898 individuals across 22 countries, we examined associations between 11 candidate childhood predictors (i.e., relationship with mother, relationship with father, parental marital status, financial status, experience of abuse, feeling like an outsider, childhood health, immigration status, religious service attendance, gender, age) with two prosocial behaviors in adulthood, charitable giving and helping strangers, and whether these associations varied by country. Random effects meta-analyses pooling estimates across all 22 countries showed evidence of associations between some candidate childhood predictors and an increased likelihood of both subsequent charitable giving and helping, and sensitivity analyses showed that associations with several (e.g., experiencing abuse, feeling like an outsider, age 12 religious service attendance) were at least moderately robust to unmeasured confounding. Of note, childhood factors did not uniformly predict both charitable giving and helping. Variations in the magnitude and direction of associations were also evident between countries, possibly reflecting diverse national influences on prosocial behaviors. With further research, these findings may inform policy and practice aimed at fostering prosociality around the world.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".