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Record W1582288474

Financing ASEAN connectivity

2014· preprint· en· W1582288474 on OpenAlexfundno aff
Fauziah Zen, Michael Regan

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersNational Institute on AgingChangchun Institute of Applied ChemistryEconomic Research InstituteElectricity Generating Authority of ThailandThailand Development Research InstituteKorea International Cooperation AgencyU.S. Department of EnergyChinese Academy of Agricultural SciencesCenters for Disease Control and PreventionAlberta Biodiversity Monitoring InstituteMinistry of EnvironmentNational Ethnic Affairs Commission of the People's Republic of ChinaMillennium Challenge CorporationMinistry of the Environment and Water Resources - SingaporeMinisterio del Ambiente, Agua y Transición EcológicaJapan International Cooperation Agency
KeywordsBusinessGeneral partnershipFinancePrivate sectorPublic–private partnershipCritical infrastructurePublic infrastructureSustainabilityProject financeSustainable developmentEconomic growthEconomic policyEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In line with the globalisation trend, it becomes inevitable for the South East Asian economies to prepare themselves to move towards the path of a more border-less and well-connected world. Evidence has shown that countries can gain a lot from internationalisation, especially from trade, knowledge and information exchanges, and flows of people and goods. One of the key targets of ASEAN in achieving a dynamic, vibrant, globally connected and strong region is to fully realise ASEAN Connectivity which consists of three pillars, namely, physical connectivity, people-to-people connectivity, and institutional connectivity. Physical connectivity is especially important because it is not only a means to connect places in South East Asia but is also vital to support people-to-people and institutional connectivity. This is documented and highlighted in several leading studies and reports, in particular, the Master Plan on ASEAN Connectivity (MPAC) and the Comprehensive Asia Development Plan (CADP).

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.080
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0800.012

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 head0.083
GPT teacher head0.287
Teacher spread0.204 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2014
Admission routes1
Has abstractyes

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