Enhancing economic growth through digital financial inclusion: An examination of India
Bibliographic record
Abstract
Financial technology has propelled India's financial sector to international acclaim. The rise of the digital economy has played a crucial role in fueling the nation's ongoing economic growth and prosperity. Using the CRITIC approach, this report thoroughly analyses FinTech and digital economy metrics in all 28 states of India from 2010 to 2022. A thorough analysis of this data uncovers the complex relationships and interactions between FinTech and the digital economy. The findings clearly demonstrate the significant impact of FinTech on India's digital economy. One important result of this impact is the progress of technological advancements, along with a decrease in the financial independence of local governments. In addition, the study reveals a fascinating finding: the influence of FinTech on the growth of the digital economy is enhanced by the existence of local financial regulatory mechanisms. By strengthening regulatory resources, FinTech plays a crucial role in promoting the development of the digital economy, especially in economically advanced regions. This research utilizes a cutting-edge methodology to unravel these intricate phenomena, providing new perspectives on the interaction between FinTech and the digital economy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".