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Record W4281651510 · doi:10.18280/ijsdp.170327

Attracting Investment Capital to Help Develop the Economy of Countries in General and Attractive Localities in Particular

2022· article· en· W4281651510 on OpenAlexvenueno aff
Nga Pham, Huong Thi Pham

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Regional Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)BusinessIncentiveCapital (architecture)Distribution (mathematics)Government (linguistics)LocalityResource (disambiguation)Return on investmentCapital investmentEconomic growthFinanceEconomicsGeographyMarket economyPolitical scienceComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

Attracting investment capital to help develop the economy of countries in general and attractive localities in particular. This study was carried out to evaluate the impact of the distribution of local attributes on attracting investment capital in the Thai Nguyen province of Vietnam. The research method is conducted through quantitative analysis with 150 enterprises surveyed in the locality. With multivariable analysis technique (reliability test, PLS-SEM model analysis) on Smart-PLS version 3 software. Research results from 150 business enterprises in Thai Nguyen (a province of Vietnam). Vietnam) shows that all four groups of local attributes influence investment decisions in the area as well as investor satisfaction: (1) investment incentives; (2) government support; (3) skill training; and (4) living environment. From the results of this study, the authors make some recommendations to help Thai Nguyen province better attract investment capital based on appropriate local resource allocation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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