Impact of Financial Inclusion on India’s Economic Development under the Moderating Effect of Internet Subscribers
Bibliographic record
Abstract
Financial inclusion is an emerging economic growth paradigm, especially in developing economies like India. It is an essential barometer for the all-encompassing growth of a country and its economy. However, there is still a debate regarding the effect of Financial Inclusion (FI) on achieving sustainable development. This study aims to determine if FI helps achieve Sustainable Development Growth (SDG) in India and if internet subscribers significantly influence the connection between FI and SDG. Secondary data from 16 states and one UT in India have been collected for 2017–2019. Therefore, the sample data is recent and covers a large country span. The data source is NITI Aayog and PMFBY (“Pradhan Mantri Fasal Bhima Yojana”) reports. The findings of this research are that FI has a positively significant relationship with sustainable development goals (SDG) in India. However, when the internet subscribers are high, the FI’s positive association with SDG gets reduced. PMFBY and SDG have been used for the first time, along with internet subscribers as moderators. The outcome has direct policy implications for improving the nation’s financial inclusion and economic growth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".