India’s Sustainable Growth Model: Policy Reforms, Digital Transformation and Resilience
Bibliographic record
Abstract
India has sustained strong economic growth despite multiple global headwinds, including the COVID-19 pandemic, geopolitical tensions, energy price shocks, and monetary tightening across advanced economies. According to the International Monetary Fund’s World Economic Outlook Update (January 2023), India’s GDP growth was projected at 6.1–6.5%, making it the fastest-growing major economy. This paper examines the structural and policy dimensions underpinning India’s resilience up to January 2023. Using secondary data from IMF, RBI, World Bank, NITI Aayog, and NPCI, the study highlights the role of fiscal-monetary coordination, digital transformation, MSME empowerment, Production Linked Incentive (PLI) schemes, and green energy initiatives. Tables and figures capture trends in GDP growth, inflation, UPI transactions, repo rates, and FDI inflows. Comparative insights with China, Vietnam, Indonesia, and Singapore are included to situate India’s global positioning. The study concludes with policy implications and a future roadmap towards India @2047, emphasizing inclusiveness, sustainability, and innovation as key pillars of long-term prosperity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".