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Record W1987495141

Impact of information technology (IT) in trade facilitation on small and medium enterprises (SMEs) in Sri Lanka

2009· preprint· en· W1987495141 on OpenAlexfundno aff
Janaka Wijayasiri, Suwendrani Jayaratne

Bibliographic record

VenueEconstor (Econstor) · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsSri lankaTrade facilitationSmall and medium-sized enterprisesBusinessInformation technologyFacilitationIndustrial organizationInternational tradeTrade barrierEconomicsFinanceManagementSocioeconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Following widespread economic reforms, India undertook focused and dedicated trade facilitation (TF) initiatives for improving infrastructure and the regulatory regime dealing with its external sector. Information technology (IT) and information technology enabled services (ITES) are prominently placed centre-stage of the trade process reforms. The Central Board of Excise and Customs (CBEC) spearhead the TF initiatives, supplemented by the Information Technology Act (2000) and eTrade Initiative of the Ministry of Commerce and Industry (MoCI). Wide coverage of the EDI/ICEGATE system and introduction of RMS are some of the key achievements, apart from new efforts to improve inter-agency connectivity through a centralized server of CBEC. These initiatives have contributed to a reduction in dwell time across ports and at air cargo points. The Systems Unit of the Customs Department periodically releases dwell time improvement details which helps the trading community in a major way. However, as this study and survey shows, the gains are not equally distributed across the trading community.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.233
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 teacher head, not a consensus.

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

Citations2
Published2009
Admission routes1
Has abstractyes

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