Mapping demographic variations in sense of mastery across the world a cross-national analysis of 22 countries in the global flourishing study
Bibliographic record
Abstract
Are certain parts of the world home to people with a higher sense of mastery? Does mastery vary across key demographic factors in similar or different ways across national contexts? These questions have been underexplored, or not explored at all. We analyzed nationally representative data from 22 countries in the Global Flourishing Study (N = 202,898) and evaluated these questions. People with the highest mastery were found in countries from Latin America (e.g., #1 Mexico, #3 Argentina), the Middle East/North Africa (e.g., #2 Egypt), and in high-income societies across different regions (e.g., #4 Israel, #5 United States, #6 Spain, #7 Sweden, #8 Hong Kong, and #9 Australia). The results indicate that a high a sense of mastery is achievable in diverse geographical and cultural contexts. Meta-analytic results that pooled country-specific estimates showed a progressive increase in mastery with age. Higher mastery was reported by people who were self-employed or employed by an employer, married, highly educated, regular attendees of religious services, and men. We also observed substantial heterogeneity in these factors across countries. Our research contributes to a more nuanced understanding of global patterns of mastery, and also suggests pathways for fostering mastery within and across diverse national contexts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".