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Record W7117130446 · doi:10.5281/zenodo.18031091

India's Evolving Role in Global Supply Chains

2025· article· W7117130446 on OpenAlexaboutno aff
Sambhaji Manikrao Gate

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainGovernment (linguistics)Key (lock)Supply and demandSupply shockQuarter (Canadian coin)Real gross domestic productExchange rate

Abstract

fetched live from OpenAlex

India's growing prominence in global supply chains is driven by its strategic location, large labor force, and government initiatives. The country's economy has demonstrated remarkable resilience, achieving an annual GDP growth of 8.15% for the fiscal year 2023-2024. With its robust domestic consumption, manufacturing expansion, and infrastructure development, India is poised to become a pivotal player in global supply chains. This paper discusses India's evolving role in global supply chains, highlighting key factors driving its growth, government initiatives, challenges, and opportunities. India's GDP growth rate is projected to be around 6.7% in 2025, driven by strong domestic demand and robust GST reforms. The economy has shown resilience, with a growth rate of 7.8% in the April-June quarter of 2025, the fastest in five quarters. Here's a breakdown of the growth projections. India has recorded a trade deficit with nine of its top 10 trading partners in FY 2023- 24.The country is working to diversify its trade relationships and strengthen economic ties with key partners. India's export growth potential is vast, with projections indicating a significant increase in exports driven by various factors. Key highlights include.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0020.002
Scholarly communication0.0090.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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
Published2025
Admission routes1
Has abstractyes

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