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Record W3204553947 · doi:10.17755/esosder.864792

THE EVALUATION OF THE IMPACT OF SOCIAL CAPITAL ON ECONOMIC DEVELOPMENT WITHIN THE FRAMEWORK OF THE LEGATUM PROSPERITY INDEX: THE CASE OF OECD COUNTRIES

2021· article· en· W3204553947 on OpenAlexaboutno aff
Eylül Kabakçı Günay, Dilara Sülün

Bibliographic record

VenueElektronik Sosyal Bilimler Dergisi · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityIndex (typography)Social capitalWelfareEconomic growthGeographyDevelopment economicsEconomicsDemographic economicsPolitical scienceEconomy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.325
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

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