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Record W2624491130 · doi:10.21013/jmss.v7.n2.p18

An Impact of Modi’s Government on Indian Economy with Special Reference to Selected Governmental Programmes

2017· article· en· W2624491130 on OpenAlexaboutno aff
N. Prakasha

Bibliographic record

VenueIRA-International Journal of Management & Social Sciences (ISSN 2455-2267) · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasing power parityEconomyWorld economyEconomicsPopulationChinaInvestment (military)Purchasing powerQuarter (Canadian coin)GeographyPolitical scienceExchange ratePolitics

Abstract

fetched live from OpenAlex

The economy of India is the sixth largest economy in the world measured by Purchasing Power Parity (PPP). The country is classified as a newly industrialized country, one of the G-20 major economies, a member of BRICS and a developing economy with an average growth rate of approximately 7 percent over the last two decades. India’s economy became the world fastest growing major economy in the last quarter of 2014, surpassing the People’s Republic of China. The long-term growth perspective of the Indian economy is positive due its young population, corresponding low dependency ratio, healthy saving and investment rates, and increasing integration into the global economy. This paper deals with the impact of Narendra Modi’s Government on various socio economic sectors of Indian economy. This study is trying to analyse the progressively changes in various economic variables through implementing various welfare programmes in present Indian economy and after the Modi came into power.

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)0.003
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.040
GPT teacher head0.298
Teacher spread0.258 · 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
Published2017
Admission routes1
Has abstractyes

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