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Record W1990331496 · doi:10.1162/asep_a_00009

Measuring Economic Integration in the Asia-Pacific Region: A Principal Components Approach

2010· article· en· W1990331496 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAsian Economic Papers · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsAsia Pacific Foundation of Canada
Fundersnot available
KeywordsConvergence (economics)Index (typography)TourismComposite indexEconomic integrationForeign direct investmentChinaSample (material)EconomicsPrincipal component analysisEconomic indicatorInvestment (military)GeographyComposite indicatorEconomyEconomic geographyInternational economicsEconometricsMathematicsEconomic growthStatisticsMacroeconomicsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This paper measures economic integration in the Asia-Pacific (AP) region using a composite index. The weights of the index are obtained from a two-stage principal component analysis. In the first stage, we obtain a convergence index to measure the extent of convergence among the main macroeconomic indicators of a sample of AP economies. In the second stage, we use indicators of trade, FDI, and tourism, as well as the convergence index, to compute the weights for the composite index. We found that economic convergence in the AP region increased until 1998 but has since fallen back. The integration of trade, investment, and people flows increased between 1990 and 2000, weakened slightly to 2003, and has since picked up again. Among the 17 sample economies, Singapore, Hong Kong, and Chinese Taipei are the most integrated with the AP region and Indonesia and China are the least integrated.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.050
GPT teacher head0.204
Teacher spread0.153 · 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