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Record W3143210107

GOVERNMENT EFFECTIVENESS, EDUCATION, ECONOMIC DEVELOPMENT AND WELL-BEING: ANALYSIS OF EUROPEAN COUNTRIES IN COMPARISON WITH THE UNITED STATES AND CANADA, 2000-2007

2009· article· en· W3143210107 on OpenAlexaboutno aff
Guisán, María-Carmen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)Per capitaEconomicsGovernment (linguistics)Human Development IndexCorporate governanceEconometric modelPer capita incomeEconomic growthDevelopment economicsPolitical scienceHuman development (humanity)EconometricsSociologyPopulationFinanceDemography
DOInot available

Abstract

fetched live from OpenAlex

In this article we present an econometric analysis of the relationship between several indicators of economic development and wellbeing in Europe, the United States and Canada. We calculate a compound index of several indicators based on three groups: 1) Life satisfaction and income per capita, 2) governance indicators based on World Bank, including Voice and Accounting Index and Government Effectiveness Index, and 3) Educational indicators, including public education expenditure per capita and average total years of schooling, The most outstanding countries in the overall index are Norway, Denmark, Sweden, the United States, Austria, Ireland, Switzerland, Canada, Finland, Netherlands, and the United Kingdom. The three groups of indicators are highly correlated in several ways, due to the important positive effects of education on economic development and governance effectiveness, as well as to the positive effects of the “Voice and Accounting” index on “Governance Effectiveness”, and the importance of the latter for economic development, as it is shown in the estimated econometric models. In section 2 we present an interesting summary of main factors of economic development, based on the several selected econometric models applied to international comparisons.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2009
Admission routes1
Has abstractyes

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