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Record W4250452500 · doi:10.35940/ijrte.d7944.118419

The Impact of Foreign Direct Investment Inflowson Balance of Payment

2019· article· en· W4250452500 on OpenAlexaboutno aff
Lalit Bhardwaj, Rohit Sood, Naidu Mandala, Kavumpurathu Raman Thankappan

Bibliographic record

VenueInternational Journal of Recent Technology and Engineering (IJRTE) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsBalance of paymentsCurrent accountForeign direct investmentBalance (ability)EconomicsPaymentCapital (architecture)Investment (military)GlobalizationQuarter (Canadian coin)International economicsMonetary economicsBusinessMarket economyMacroeconomicsFinanceExchange rateGeographyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

India with a powerful development rate is currently triumph more acclimatized with world economy. The cross outskirts are vague in a monetary market and have made influence on the Indian economy too. India after globalization has now supported the crosswise over outskirts trade and consequently has progressed with the monetary development. Moreover, we have tried to relationship exists between a portion of the factors like current account and merchandise and ventures, Foreign Direct Investment and between Capital account inflows. The examination explores the effect of Foreign Direct Investment on India's Balance of Payment for a time of 2012-2016 quarter savvy. Optional information will be made through RBI site, Journals, Research articles and papers. The investigation utilizes Regression to organize connection between dependent and free factors. Here foreign direct investment, the current and capital account as logical factors, while the balance of payments is the needy variable. The investigation is drawn for the balance of payments and a logical end is drawn for the connection of balance of payments with the free factors.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.238

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 head0.008
GPT teacher head0.222
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 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
Published2019
Admission routes1
Has abstractyes

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