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Record W2072301051 · doi:10.1142/s0217590810003663

ENHANCING ECONOMIC INTEGRATION IN SOUTH ASIA: ISSUES AND PROSPECTS FOR CLOSER MONETARY COOPERATION

2010· article· en· W2072301051 on OpenAlexaboutno aff
Sisira Jayasuriya, Nephil Matangi Maskay

Bibliographic record

VenueThe Singapore Economic Review · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsLiberalizationCurrencyEconomicsEconomic integrationInternational economicsRegional integrationPoliticsInternational tradeInvestment (military)Quarter (Canadian coin)Common currencyMonetary economicsPolitical scienceMarket economyGeography

Abstract

fetched live from OpenAlex

Though SAARC has the stated goal of an economic union and a common currency, after a quarter of a century, these remain distant goals as political tensions between India and Pakistan have hindered any real progress on a regional scale. Barriers to trade and factor mobility are high in the region as a whole, though considerable liberalisation has been achieved through various bilateral agreements involving India and some of its neighbours. The conventional economic conditions for a common currency are also currently absent as patterns of shocks are non-synchronised, trade links are weak and factor mobility much constrained. Deeper intraregional economic integration requires much more comprehensive trade and investment liberalisation among member nations. While the political conditions for a single currency are unlikely to emerge in the foreseeable future, steps towards closer monetary cooperation through a South Asian Monetary System — building on the existing SAARCFINANCE network — may provide an institutional framework for enhancing regional integration. However, such cooperation will have to be conceived as a component of a sustainable transitional strategy which commits to a serious programme of deeper trade liberalisation to facilitate greater integration with the rest of the world, and most importantly, with East Asia.

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)0.011
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0010.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.022
GPT teacher head0.261
Teacher spread0.238 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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