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Record W2381756394

Measurement and Prediction on the Degree of Freedom of China's Economic——Based on the View of EFW Idex Compiled by Fraser Institute of Canada

2008· article· en· W2381756394 on OpenAlexaboutno aff
Shi-Zhuan Han

Bibliographic record

VenueTongji yu xinxi luntan · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGeochemistry and Geochronology of Asian Mineral Deposits
Canadian institutionsnot available
Fundersnot available
KeywordsChinaIndex (typography)Economic freedomPer capitaIndex of Economic FreedomRegression analysisLogarithmClassical economicsLinear regressionEconomicsEconometricsEconomyPolitical scienceStatisticsMathematicsLawSociologyDemographyComputer sciencePopulation
DOInot available

Abstract

fetched live from OpenAlex

The of china's economic freedom is always widely argued.In this paper the author focuses on the introduction of degree of freedom of world economy of EFW Index and their assessment on China's of market.After a comparison of various assessment critiron,the author concluded that the EFW Index can most properly measure the economic of freedom.The author find out that the logarithm of China's GDP per capita could fit China's economic of freedom oriented from EFW Index in a fine way and based on this view the author builds a regression equation and predicts China's EFW Index for the future 25 years.The regression equation is:China's chain linked EFW Idex=1.23+0.686Ln(GDP per capita).Based on the regression analysis,the author predicts the years in which China will catch up with certain country's of freedom of 2005.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.880
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.175
Teacher spread0.146 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2008
Admission routes1
Has abstractyes

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