The psychometric network of individual flourishing across nationally representative samples from 22 countries
Bibliographic record
Abstract
To effectively promote human flourishing, it is important to understand how the different dimensions of flourishing might be related to one another in different sociocultural contexts. Applying a systems perspective to flourishing, this study uses nationally representative survey cross-sectional data from 22 geographically and culturally diverse countries included in the Global Flourishing Study (N = 202,898) to explore the interrelatedness of the components of individual flourishing captured by the Secure Flourish Measure. A meta-analytic gaussian network aggregation (MAGNA) model was applied to investigate similarities and differences among the interrelations of individual flourishing components across countries. Results revealed a network of mostly positive interrelations among the 12 components, although there was substantial heterogeneity in the strength of associations, especially between life satisfaction, happiness, and mental health. Understanding cross-country differences in light of socio-contextual peculiarities will be crucial for informing the development of targeted interventions to promote flourishing.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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".