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Record W3153983289 · doi:10.5267/j.ac.2021.3.024

Attraction of foreign direct investment in agriculture

2021· article· en· W3153983289 on OpenAlexvenueno aff
Tien Do Thi Kim

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGeochemistry and Geochronology of Asian Mineral Deposits
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentAgricultureVietnameseProduction (economics)Agricultural productivityInvestment (military)BusinessCapital (architecture)International economicsInternational tradeEconomicsAgricultural economicsGeographyMacroeconomicsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Agriculture is an industry with potential and advantages for development, but it is increasingly difficult to attract foreign direct investment (FDI) flows. Up to now, the results of attracting FDI inflows into the agricultural sector have many limitations, not really reaching the industry’s potential. This study will assess the current situation of attracting foreign direct investments into the agricultural sector in Vietnam in terms of FDI capital scale, FDI capital structure based on agriculture standard, investment method, investment partners and by investment recipients. The Red River Delta is one of the two Vietnamese economic regions with highly agricultural production. With the tradition of agricultural production and many favorable natural, economic and social conditions, the Red River Delta can further develop into a major agricultural production area of the country, contributing to economic development of the region and the whole country. However, FDI investment in agriculture in the region is modest compared to the potential of the industry as well as compared to other sectors in the region. While FDI inflow into Vietnam and other sectors in the region tends to increase strongly, FDI into agriculture is very low and has not grown for a long time, which is contrary to the trend of FDI to other sectors of the Red River Delta as well as the whole country and also contrary to the FDI flows to global agriculture.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.281
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.192
Teacher spread0.178 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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