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Record W3116764784 · doi:10.5539/ijef.v13n1p111

The Effect of Foreign Direct Investment on the Hospitality Industry in Liberia: A Case Study on the Chinese Investment

2020· article· en· W3116764784 on OpenAlexvenueno aff
Loretha Adjuah Blamoh, Jingyi Yang, Wen Meixue, Gao Weiijie

Bibliographic record

VenueInternational Journal of Economics and Finance · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Management and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentDeveloping countryTourismBusinessNatural resourceHospitality industryInvestment (military)HospitalityEconomic growthEconomicsPolitical science

Abstract

fetched live from OpenAlex

Foreign Direct Investment is said to have a positive impact on the development of the hospitality industry in developing countries. It helps create employment opportunities and positively impact local economies. The present study research published articles, desk reviews, scientific databases among others to report the results. Current findings showed that the success of the hospitality industry in developing countries depends on the levels of Foreign Direct Investment. Although many developing countries have natural features such as beaches, rivers, and other natural resources, local capital to invest in those resources is unavailable. Tourism shows particular promise for developing countries. The tourism industry is one of the largest and fastest-growing sectors in the global economy and a key driver of socio-economic development, as it is labor-intensive and stimulates SME growth and investment. It has been used in other countries as an economic driver for growth which can widely support poverty reduction.Foreign Direct Investment is said to have a positive impact on the development of the hospitality industry in developing countries. It helps create employment opportunities and positively impact local economies. The present study research published articles, desk reviews, scientific databases among others to report the results. Current findings showed that the success of the hospitality industry in developing countries depends on the levels of Foreign Direct Investment. Although many developing countries have natural features such as beaches, rivers, and other natural resources, local capital to invest in those resources is unavailable. Tourism shows particular promise for developing countries. The tourism industry is one of the largest and fastest-growing sectors in the global economy and a key driver of socio-economic development, as it is labor-intensive and stimulates SME growth and investment. It has been used in other countries as an economic driver for growth which can widely support poverty reduction.

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)0.002
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.227
Teacher spread0.204 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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