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Record W2990259472 · doi:10.1111/twec.12907

Trade configurations in Asia: Assessing de facto and de jure regionalism

2019· article· en· W2990259472 on OpenAlexaff
Sasidaran Gopalan, Luu Nguyen Trieu Duong, Ramkishen S. Rajan

Bibliographic record

VenueWorld Economy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDe factoRegionalism (politics)NegotiationInternational tradeScope (computer science)Economic integrationEconomicsGeneral partnershipInternational economicsChinaPolitical science

Abstract

fetched live from OpenAlex

Abstract Given the backdrop of significant uncertainties largely propelled by the ongoing trade spat between the United States and China, to what degree can the Asian region move forward in terms of de facto trade integration? Drawing on the new economic geography literature, this paper offers new insights into the literature on trade regionalism in Asia by empirically illustrating how Asian economies can tap into the regional market potential. Specifically, the paper examines the scope for further de facto integration among the Asian countries engaged in the negotiation for the Regional Comprehensive Economic Partnership (RCEP) Agreement by estimating foreign market potential (FMP) indices. To preview the main findings, the empirical analysis demonstrates that the share of intra‐regional trade in total RCEP trade flows and, consequently, the ratio between within‐ and outside‐RCEP trades are significantly lower than what they could potentially be. The paper makes a case that the enhancement of de jure integration among these economies through the RCEP must be accompanied by efforts to improve de facto integration.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0010.001
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 head0.045
GPT teacher head0.229
Teacher spread0.184 · 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 designTheoretical or conceptual
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

Citations4
Published2019
Admission routes1
Has abstractyes

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