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The Impact of International Business Transitions on India: Opportunities and Challenges

2024· article· en· W4391844969 on OpenAlexaff
Vaishnavi Lavankar -, Yuvraj Jadhav -, Prof. Shantilal Jadhav -

Bibliographic record

VenueInternational Journal For Multidisciplinary Research · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsTrinity College
Fundersnot available
KeywordsInternational businessBusinessEconomic geographyGeographyManagementEconomics

Abstract

fetched live from OpenAlex

International business transitions are becoming more important for emerging countries like India as globalization continues to transform the global economy. Particularly in the previous three decades, Indian commercial firms, innovation, and entrepreneurship have undergone numerous transformations. The biggest shift came about as a result of national government policies that moved away from postcolonial Nehruvian socialism and opened up more economic opportunities for private companies and entrepreneurs. The decade of the 1990s marked a turning point for these momentous shifts. of the 1990s, Indian business experienced an incredible surge of energy and passion that had never been seen before in the post-Independence era. This study looks at how international business shifts affect India, highlighting the benefits and drawbacks of globalization. The study assesses how global business shifts have affected India's economy, society, and business environment. It also addresses the policies and tactics required for India to successfully manage these changes, maximize their potential advantages, and reduce any hazards involved.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0040.003
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.280
GPT teacher head0.417
Teacher spread0.137 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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