Social Capital, Trust and Well-being in the Evaluation of Wealth
Why this work is in the frame
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Bibliographic record
Abstract
We combine theory with data from different domains to provide an empirical analysis of the scale and variability of social capital as wealth. This is used to argue, given what we have learned in the literature on social capital, that the welfare returns to investing in trust could be substantial. Using social trust data from 132 nations covered by the Gallup World Poll, we present a range of estimates of social trust's wealth-equivalent values. The estimates of the wealth embodied in social capital are very large, and with a structure and distribution quite different from those for physical capital. These estimates reflect values above and beyond what social trust contributes to supporting incomes and health. Although social trust is an important component of total wealth in all regions and country groupings, there are nonetheless big variations within and among regions, ranging from as low as 12% of total wealth in Latin America to 28% in the OECD.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it