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Record W2796213271 · doi:10.24114/qej.v1i4.17415

ANALISIS PENGARUH PERDAGANGAN INTRA-REGIONAL DAN EKTRA-REGIONAL ASEAN TERHADAP PERTUMBUHAN EKONOMI NEGARA-NEGARA ASEAN-5

2020· article· en· W2796213271 on OpenAlexaff
Baida Soraya

Bibliographic record

VenueQuantitative Economics Journal · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsEconomic integrationRegional tradeEconomicsProsperityForeign direct investmentInternational tradeRegional integrationInternational economicsInternational free trade agreementInflation (cosmology)Free tradeEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

Economy growth is one of indicators of people prosperity in a country. ASEAN is a type of economic integration which aim to increase economic growth of member countries. Intra-regional and extra-regional trade is kind of trade agreement which aim to increase the trade rate and economic growth. However, the rate of extra-regional trade in every ASEAN-5 countries is higher than intra-regional trade. The objective of this research is to analyze the factors which effect the economic growth of ASEAN-5 countries during 2007-2011. With random effect model in pooled data processing, the research result described that extra-regional trade of ASEAN, foreign direct investment, inflation, and the population described positive and significant effect to economic growth of every ASEAN-5 countries. Whereas, intra-regional trade of ASEAN effect positive and insignificant to the economic growth of ASEAN-5 countries.

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)0.002
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.105
GPT teacher head0.259
Teacher spread0.154 · 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
Published2020
Admission routes1
Has abstractyes

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