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Record W2908435530 · doi:10.5267/j.msl.2018.12.006

Economic linkage in key economic zones: The case of Vietnam

2018· article· en· W2908435530 on OpenAlexvenueno aff
Thi Hong Linh Phi, Manh Dung Tran, Van Hoa Hoang, Vu Hiep Hoang

Bibliographic record

VenueManagement Science Letters · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLinkage (software)Key (lock)Special economic zoneBusinessComputer scienceGeographyComputer securityChinaChemistryArchaeology

Abstract

fetched live from OpenAlex

This study was conducted to measure the level of economic linkage in key economic zones in central Vietnam generally and to measure the level of economic linkage in the tourism sector.Secondary data were collected from the Statistical Yearbook published by the General Statistics Office and the Local Bureau of Statistics and primary data collected from surveys of 102 hotels and tourism firms in the center of Vietnam.We used the Moran Index (I) to quantify overall economic aggregation across the region and that scoring method was used to measure the degree of linkage in regional tourism.The results show that the level of overall regional economic linkages was very low and this was also relevant when analyzing the tourism sector in some areas of the center of Vietnam.Based on the results, some recommendations are given for strengthening economic linkages in key economic regions of 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)0.001
metaresearch head score (Gemma)0.003
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 score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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