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Record W1585566927

Overseas Investment Attraction for the Companies in Madagascar, Due To Low Wage to Employee and Abundant Natural Resources

2014· article· en· W1585566927 on OpenAlexaboutno aff
Rakotonirina Jeremy Desiré, Jinhua Cheng

Bibliographic record

VenueResearch on humanities and social sciences · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsPaceNatural resourcePovertyInvestment (military)BusinessDeveloping countryHuman resourcesChinaForeign direct investmentUnemploymentEconomicsEconomic growthDevelopment economicsFinanceGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the information about the natural resources in Madagascar, their location and about the regulatory framework. The Madagascar is a country which is rich in minerals resources. So in this paper proposal has been proposed to some investors to invest in Madagascar. As Madagascar is a developing country, international investors can gain more profits as to invest in the country due to low wage to employees.  Regulatory framework about the investment in the Madagascar has been discussed.  We can say nature has blessed abundant natural resources to Madagascar so that development in this sector can turn Madagascar’s economic instability to developed financial and economic stability. But due to lack of better policies or may be the present policies implementation problem, lack of funds, lack of human resource, lack of machinery, lack of expert in this sector is not getting the pace as desired. Being a developed or having a strong GDP rate some countries like China, Canada, Australia, USA and some other countries can do a lot for the development of this sector in Madagascar, so that the interested companies can come to Madagascar for the consideration of mineral re-sources, which can result into the economic development of both countries and as a repercussion Madagascar can cope up lots of problem which it is facing, e.g. economic in-stability, indebted of IMF and World Bank, unemployment, poverty, lack of technical education etc. On the other side foreign investors and companies can improve their setup on large scale, their development and man power. As a moral of story through the progress in mineral resources can make Madagascar advanced and established country. Keyword: Minerals, Madagascar, Tourism, Regulatory framework, Investment

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0350.002

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 head0.151
GPT teacher head0.364
Teacher spread0.214 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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