Understanding who volunteers globally through an examination of demographic variation in volunteering across 22 countries
Bibliographic record
Abstract
Volunteering has been associated with enhanced individual and societal well-being around the world. While some prior research has assessed cultural and sociodemographic differences in volunteering, we know little about how volunteering differs across sociodemographic indicators cross-nationally. Using data from the Global Flourishing Study, a diverse and international sample of 202,898 individuals across 22 countries, we examined the distribution of volunteering across demographic factors (age, gender, marital status, employment status, religious service attendance, education, and immigration status) and across countries. We found variation in volunteering across demographic groups and countries. Unadjusted proportions of volunteering varied between countries: Nigeria showed the highest proportion (0.51) followed by Indonesia (0.46) and Kenya (0.40), while Japan (0.09), Poland (0.08), and Egypt (0.04) showed the lowest proportions of volunteering. Random effects meta-analyses showed that the proportion of people who volunteered differed between demographic groups (e.g., volunteering was higher among those with more education and religious service attendance). Because of the growing evidence of substantial contributions of volunteering to individual as well as societal well-being, it is increasingly important for organizations, governments, and public health officials alike to consider ways to ensure accessibility for able and willing volunteers.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".