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Record W3083676084 · doi:10.5430/rwe.v11n5p34

Analysing ICT Economic Impact in Vietnam

2020· article· en· W3083676084 on OpenAlexvenueno aff
Đặng Thị Việt Đức, Dang Huyen Linh

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologySpillover effectBusinessEconomic sectorFinal demandEconomicsIndustrial organizationEconomyProduction (economics)MacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

This article applied the input-out table (IO) analysis to estimate the linkages of ICT sectors to the Vietnam economy. In this article, the shared output requirement of ICT sectors is analysed to the multiplier effect, inter-sector feedback effect, and spillover effect. The research also examines the induced increase of ICT's output to the final demand of ICT sectors and non-ICT sectors. The results show that although the impact of the domestic ICT sectors in the Vietnam economy increases through time, it is generally not outstanding in comparison with other sectors. The ICT manufacturing sector is rather self-sufficient, stimulating import rather than added value for the domestic economy. From both the intermediate and final demand inducement, ICT media, content and ICT services sectors reveal their significant diffusion and critical inter-sector relationship with other ICT and non-ICT sectors in the economy. The paper also provides policy implications for the future development of ICT in Vietnam.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.159
GPT teacher head0.353
Teacher spread0.194 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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