THE EVALUATION OF THE IMPACT OF SOCIAL CAPITAL ON ECONOMIC DEVELOPMENT WITHIN THE FRAMEWORK OF THE LEGATUM PROSPERITY INDEX: THE CASE OF OECD COUNTRIES
Bibliographic record
Abstract
Social capital is one of the variables that influences overcoming problems related to welfare and development. Studies show that social capital level is generally in a linear relationship with the development level of countries. The purpose of study is to examine the relationship between the welfare levels of OECD countries and their social capital levels within the framework of the Legatum Prosperity Index. For this, the effect of social capital, which is one of the 12 components of the Legatum Prosperity index, on the welfare level of OECD countries was examined. the country rankings in which social capital is equally weighted with other index variables and the country rankings where social capital is excluded from the index are compared. The results obtained show that social capital is improving for the rankings of the welfare levels of Norway, Denmark, Iceland, New Zealand, Canada, Australia, USA, Slovenia, Portugal, Israel, Slovakia; has deteriorating effect for Switzerland, United Kingdom, Luxembourg, France, Belgium, Hungary, Czech Republic, Greece, Mexico, Latvia, Japan, Lithuania, South Korea and Turkey. Social capital variable alone did not make a difference in the welfare level rankings of Finland, Netherlands, Sweden, Austria, Ireland, Germany, Spain, Estonia, Italy, Chile, Colombia and Poland.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".