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Record W4283370021 · doi:10.5430/rwe.v13n1p19

Perennial Plants in Vietnam's Economy

2022· article· en· W4283370021 on OpenAlexvenueno aff
Duong Mạnh Hung, Bùi Trinh

Bibliographic record

VenueResearch in World Economy · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsPerennial plantAgricultureAgricultural economicsEconomicsChinaProduction (economics)ProductivityCropAgroforestryBusinessNatural resource economicsEconomyAgronomyEnvironmental scienceGeographyForestryEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

Vietnam has strengths in perennial crop production due to its diverse soil, climate and crop ecosystems. Developing perennial crops is strength of our Vietnam's agriculture to serve the requirements of raw materials for the processing industry and for export. During the 36 years of renovation (1995 - 2021), perennial crop production has continuously developed comprehensively, growing rapidly both in terms of area expansion and intensive farming to increase productivity and output. In recent years, the output of most perennial crops has increased sharply, especially those associated with export such as coffee, rubber, tea, cashew, and pepper. Policymakers and many researchers in Vietnam seem to be "crazy" for the GDP index, so everything seems to be compared with GDP; if an industry's share in GDP is low, it doesn't seem these sectors important enough! This study used input-output analysis method to show the importance of perennial crops to the Vietnamese economy through the multiplier links between industries (inter-industrial) and the economy's supply-demand relationship.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.064
GPT teacher head0.303
Teacher spread0.239 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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